Process for case-based insurance underwriting suitable for use by an automated system

ABSTRACT

A process for at least a partial underwriting of insurance policies is described. Based on the similarity to previous insurance applications, a decision on the current request for underwriting may be made. This decision-making process represents an analogical approach to the placement of an insurance application to an underwriting category, whereby a given insurance application request is compared to previous requests.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority from U.S. ProvisionalPatent Application Serial No. 60/343,176, which was filed on Dec. 31,2001.

BACKGROUND OF THE INVENTION

[0002] The present invention relates to a process for underwritinginsurance applications, and more particularly to a process forunderwriting insurance applications based on a comparison with previousunderwritten applications.

[0003] A trained individual or individuals traditionally performinsurance underwriting. A given application for insurance (also referredto as an “insurance application”) may be compared against a plurality ofunderwriting standards set by an insurance company. The insuranceapplication may be classified into one of a plurality of risk categoriesavailable for a type of insurance coverage requested by an applicant.The risk categories then affect a premium paid by the applicant, e.g.,the higher the risk category, the higher the premium. A decision toaccept or reject the application for insurance may also be part of thisrisk classification, as risks above a certain tolerance level set by theinsurance company may simply be rejected.

[0004] There can be a large amount of variability in the insuranceunderwriting process when performed by individual underwriters.Typically, underwriting standards cannot cover all possible cases andvariations of an application for insurance. The underwriting standardsmay even be self-contradictory or ambiguous, leading to uncertainapplication of the standards. The subjective judgment of the underwriterwill almost always play a role in the process. Variation in factors suchas underwriter training and experience, and a multitude of other effectscan cause different underwriters to issue different, inconsistentdecisions. Sometimes these decisions can be in disagreement with theestablished underwriting standards of the insurance company, whilesometimes they can fall into a “gray area” not explicitly covered by theunderwriting standards.

[0005] Further, there may be an occasion in which an underwriter'sdecision could still be considered correct, even if it disagrees withthe written underwriting standards. This situation can be caused whenthe underwriter uses his/her own experience to determine whether theunderwriting standards may or should be interpreted and/or adjusted.Different underwriters may make different determinations about whenthese adjustments are allowed, as they might apply stricter or moreliberal interpretations of the underwriting standards. Thus, thejudgment of experienced underwriters may be in conflict with the desireto consistently apply the underwriting standards.

[0006] Most of the key information required for automated insuranceunderwriting is structured and standardized. However, some sources ofinformation may be non-standard or not amenable to standardization. Byway of example, an attending physician statement (“APS”) may be almostas unique as each individual physician. However, a significant fractionof applications may require the use of one or more APS due to thepresence of medical impairments, age of applicants, or other factors.Without such key information, the application underwriting processcannot be automated for these cases.

[0007] Conventional methods for dealing with some of the problemsdescribed above have included having human underwriters directly readingthe APS. However, an APS document can be as long as several tens ofpages. Therefore, the manual reading process, combined with note-takingand consulting other information, such as an underwriting manual or thelike, can greatly extend the cycle-time for each application processed,increase underwriter variability, and limit capacity by preventing theautomation of the decision process.

[0008] Other drawbacks may also exist.

SUMMARY OF THE INVENTION

[0009] In an exemplary embodiment of the invention, a process forunderwriting a pending insurance application based on a comparison withat least one of a plurality of previous insurance applicationunderwriting decisions, comprises receiving a request to underwrite thepending insurance application, where the request includes informationabout at least one application component, retrieving a plurality ofrelevant insurance applications from the plurality of previous insuranceapplication underwriting decisions based on a similarity between the atleast one application component and at least one correspondingapplication component in the retrieved relevant insurance applicationand ranking the plurality of retrieved insurance applications accordingto a level of similarity between the at least one application componentand at least one corresponding application component in the retrievedinsurance applications. The process further comprises selecting at leastone best insurance application from the plurality of retrieved insuranceapplications, adapting a solution to the pending insurance applicationbased on the at least one best insurance application, and evaluating thesolution using a confidence factor, where the confidence factor is basedon the reliability of the solution.

[0010] Another exemplary embodiment of the invention provides a processfor rating an application, where the application includes at least onecategory, comprising determining a value for the at least one categoryof the application, creating a query based on the value of the at leastone category, retrieving at least one case, and assigning theapplication a rate classification based on the at least one retrievedcase.

[0011] In another embodiment of the invention, a process for rating aninsurance application, where the insurance application includes at leastone category, comprises determining a value for the at least onecategory of the insurance application and creating a query based on thevalue of the at least one category. Further, the process comprisesretrieving at least one insurance application case from an insuranceapplication case database, assigning the insurance application a rateclassification based on the at least one retrieved insurance applicationcase, and storing the application in the insurance application casedatabase once the rate classification has been assigned.

[0012] Another exemplary embodiment of the invention provides a computerprogram stored on a computer readable medium, wherein the computerprogram causes a computer to perform the steps of receiving a request tounderwrite the pending insurance application, where the request includesinformation about at least one application component, retrieving aplurality of relevant insurance applications from the plurality ofprevious insurance application underwriting decisions based on asimilarity between the at least one application component and at leastone corresponding application component in the retrieved relevantinsurance application, and ranking the plurality of retrieved insuranceapplications according to a level of similarity between the at least oneapplication component and at least one corresponding applicationcomponent in the retrieved insurance applications. The computer codefurther causes a computer to perform the steps of selecting at least onebest insurance application from the plurality of retrieved insuranceapplications, adapting a solution to the pending insurance applicationbased on the at least one best insurance application, and evaluating thesolution using a confidence factor, where the confidence factor is basedon the reliability of the solution.

[0013] In an additional embodiment of the invention, a computer programstored on a computer readable medium causes a computer to perform thesteps of determining a value for the at least one category of theapplication, creating a query based on the value of the at least onecategory, retrieving at least one case, and assigning the application arate classification based on the at least one retrieved case.

[0014] In a further embodiment of the invention, a process forunderwriting a transaction oriented process based on a comparison withat least one of a plurality of previous transaction underwritingdecisions comprises receiving a request to underwrite the transaction,where the request includes information about at least one transactioncomponent, retrieving a plurality of relevant transactions from theplurality of previous transaction underwriting decisions based on asimilarity between the at least one transaction component and at leastone corresponding transaction component in the retrieved relevanttransaction, wherein the step of retrieving employs at least one fuzzylogic rule to retrieve plurality of previous transaction underwritingdecisions, and ranking the plurality of retrieved transactions accordingto a level of similarity between the at least one transaction componentand at least one corresponding transaction component in the retrievedtransactions The process further comprises selecting at least one besttransaction from the plurality of retrieved transactions, adapting asolution to the pending transaction based on the at least one bestinsurance transaction, evaluating the solution using a confidencefactor, where the confidence factor is based on the reliability of thesolution. storing the solution for the transactions in a case memory,wherein the case memory also stores the plurality of previoustransactions underwriting decisions, and transmitting the results of theunderwriting decision.

BRIEF DESCRIPTION OF THE FIGURES

[0015]FIG. 1 is a graph illustrating a fuzzy (or soft) constraint, afunction defining for each value of the abscissa the degree ofsatisfaction for a fuzzy rule, according to an embodiment of theinvention.

[0016]FIG. 2 is a graph illustrating the measurements based on thedegree of satisfaction for a collection of fuzzy rules, according to anembodiment of the invention.

[0017]FIG. 3 is a schematic representation of an object-oriented systemto determine the degree of satisfaction for a collection of fuzzy rules,according to an embodiment of the invention.

[0018]FIG. 4 is a flowchart illustrating steps performed in a processfor underwriting an insurance application using fuzzy logic according toan embodiment of the invention.

[0019]FIG. 5 is a flowchart illustrating steps for an inference cycleaccording to an embodiment of the invention.

[0020]FIG. 6 is a graph illustrating a fuzzy (or soft) constraint, afunction defining for each value of the abscissa the degree ofsatisfaction for a rule comparing similar cases, according to anembodiment of the invention.

[0021]FIG. 7 is a graph illustrating the core of a fuzzy (or soft)constraint, according to an embodiment of the invention.

[0022]FIG. 8 is a graph illustrating the support of a fuzzy (or soft)constraint, according to an embodiment of the invention.

[0023]FIG. 9 is a graph illustrating the rate class histogram derivedfrom a set of retrieved cases, according to an embodiment of theinvention.

[0024]FIG. 10 is a chart illustrating the distribution of similaritymeasures for a set of retrieved cases, according to an embodiment of theinvention.

[0025]FIG. 11 is a table illustrating a linear aggregation of rateclasses, according to an embodiment of the invention.

[0026]FIG. 12 is a flowchart illustrating the steps performed in aprocess for determining the degree of confidence of an underwritingdecision based on similar cases, according to an embodiment of theinvention.

[0027]FIG. 13 is a process map illustrating a decision flow, accordingto an embodiment of the invention.

[0028]FIG. 14 illustrates a comparison matrix, according to anembodiment of the invention.

[0029]FIG. 15 illustrates a distribution of classification distances foreach bin containing a range of retrieved cases, according to anembodiment of the invention.

[0030]FIG. 16 illustrates a distribution of normalized percentage ofclassification distances for each bin containing a range of retrievedcases, according to an embodiment of the invention.

[0031]FIG. 17 illustrates a distribution of correct classification foreach bin containing a range of retrieved cases, according to anembodiment of the invention.

[0032]FIG. 18 illustrates a distribution of a performance function foreach bin containing a range of retrieved cases, according to anembodiment of the invention.

[0033]FIG. 19 illustrates a distribution of a performance function foreach bin containing a range of retrieved cases, after removing negativenumbers and normalizing the values between 0 and 1, according to anembodiment of the invention.

[0034]FIG. 20 illustrates results of a plot of the preference function(derived from FIG. 19) according to an embodiment of the invention.

[0035]FIG. 21 illustrates a computation of coverage and accuracyaccording to an embodiment of the invention.

[0036]FIG. 22 is a schematic representation of a system for underwritingaccording to an embodiment of the invention.

[0037]FIG. 23 a flowchart illustrating the steps performed for executingand manipulating a summarization tool according to an embodiment of theinvention.

[0038]FIG. 24 illustrates a graphic user interface for a summarizationtool for a general form according to an embodiment of the invention.

[0039]FIG. 25 illustrates a graphic user interface for a summarizationtool for a condition-specific form according to an embodiment of theinvention.

[0040]FIG. 26 illustrates an optimization process according to anembodiment of the invention.

[0041]FIG. 27 illustrates an example of an encoded population at a givengeneration according to an embodiment of the invention.

[0042]FIG. 28 illustrates a process schematic for an evaluation systemaccording to an embodiment of the invention.

[0043]FIG. 29 illustrates an example of the mechanics of an evolutionaryprocess according to an embodiment of the invention.

[0044]FIG. 30 is a graph illustrating a linear penalty function used inthe evaluation of the accuracy of the CBE, according to an embodiment ofthe invention.

[0045]FIG. 31 is a graph illustrating a nonlinear penalty function usedin the evaluation of the accuracy of the CBE, according to an embodimentof the invention.

DETAILED DESCRIPTION OF THE INVENTION

[0046] Reference will now be made in detail to the present preferredembodiments of the invention, examples of which are illustrated in theaccompanying drawings in which like reference characters refer tocorresponding elements.

[0047] Rules Based Reasoning

[0048] As stated above, a process and system is provided for insuranceunderwriting which is able to incorporate all of the rules in theunderwriting standards of a company, while being robust, accurate, andreliable. According to an embodiment of the invention, the process andsystem provided may be suitable for automation. Such a process andsystem may be flexible enough to adjust the underwriting standards whenappropriate. As mentioned above, each individual underwriter may havehis/her own set of interpretations of underwriting standards about whenone or more adjustments should occur. According to an embodiment of thepresent invention, rules may be incorporated while still allowing foradjustment using a fuzzy logic-based system. A fuzzy logic-based systemmay be described as a formal system of logic in which the traditionalbinary truth-values “true” and “false” are replaced by real numbers on ascale from 0 to 1. These numbers are absolute values that representintermediate truth-values for answers to questions that do not havesimple true or false, or yes or no answers. In standard binary logic, agiven rule is either satisfied (with a degree of satisfaction of 1), ornot (with a degree of satisfaction of 0), creating a sharp boundarybetween the two possible degrees of satisfaction. With fuzzy logic, agiven rule may be assigned a “partial degree of satisfaction”, a numberbetween 1 and 0, in some boundary region between a “definite yes”, and a“definite no” for the satisfaction of a given rule. Each rule will becomposed by a conjunction of conditions. Each condition will berepresented by a fuzzy set A(x), which can be interpreted as a degree ofpreference induced by a value x for satisfying a condition A. Aninference engine determines a degree of satisfaction of each conditionand an overall degree of satisfaction of a given rule.

[0049] For the purposes of illustration, imagine that a hypotheticallife insurance company has a plurality of risk categories, which areidentified as “cat1”, “cat2”, “cat3”, and “cat4.” In this example, arating of cat1 is a best or low risk, while cat4 is considered a worstor high risk. An applicant for an insurance policy would be rejected ifhe/she fails to be placed in any category. An example of a type of rulelaid out in a set of underwriting guidelines could be, “The applicantmay not be in cat1 if his/her cholesterol value is higher than X1.”Similarly, a cholesterol value of X2 could be a cutoff for cat2, and soon. However, it is possible that a cholesterol reading of one point overX1 may not in practice disqualify the applicant from the cat1 rating, ifall of the other rules are satisfied for cat1. It may be that readingsof one point over X1 are still allowable, and so on. To define a fuzzyrule, two parameters, X1a and X1b may be needed. When the applicant'scholesterol is below X1a, a fuzzy rule may be fully satisfied (e.g., adegree of satisfaction of 1). By way of present example, X1 from theabove may be used as X1a. A parameter X1b may be a cutoff above whichthe fuzzy rule is fully unsatisfied (e.g., a degree of satisfaction of0). For example, it may be determined from experienced underwriters ofthe insurance company that under no circumstances can the applicant getthe cat1 rating if his/her cholesterol is above 190 (X1) by more thanfour points. In that situation, the fuzzy rule may use X1a=X1, that is190, and X1b=X1a+4, that is 194. Other settings may be used. X1a and X1bare parameters of the model. To obtain the partial degree ofsatisfaction when the cholesterol value falls within the range [X1a,X1b], a continuous switching function may be used, which interpolatesbetween the values 1 and 0. The simplest such function is a straightline, as disclosed in FIG. 1, but other forms of interpolation may alsobe used.

[0050] Turning to cat2, cat3, and cat4, there may be a differentcholesterol rule for each category, which states that the applicant maynot be placed in that category if his/her cholesterol is higher than X2,X3, or X4, respectively. The same procedures may be used, turning eachrule into a fuzzy logic rule by assigning high and low cutoff values(e.g., X2a, X2b; X3a, X3b; X4a, X4b). Thus, by way of continuing theexample, cat2 may be associated with a fuzzy rule that uses X2a=X2 andX2b=X2+4, where X2=195 (for cat2). In addition X3a=X3 and X3b=X3+4,where X3=200 (for cat3), and X4a=X4 and X4b=X4, where X4=205 (for cat4).Other parameters also may be used. Similarly, one would proceed througheach rule in the underwriting guidelines, allowing for fuzzy partialdegrees of satisfaction. In the present invention, each piece of datamay be judged many times on the basis of each rule.

[0051] Once each fuzzy rule in the rule set has been applied, a decisionis made to which category the applicant belongs. For each risk category,there may be a subset of rules that apply to that category. In order tojudge whether the applicant is eligible for the given category, somenumber of aggregation criteria may be applied. To be concrete, using theabove hypothetical case, take the subset of all rules that apply tocat1. There will be a fuzzy degree of satisfaction for every rule, wherethe set of degrees of satisfaction is called {DS−cat1}. According to anembodiment of the invention, if any of the degrees of satisfaction arezero, then the applicant may be ruled out of cat1. Thus, one of theaggregation criteria may be, “reject from cat1 if MIN({DS−cat1})<=A1,”where A1 is a chosen constant, and the notation MIN( . . . ) denotesselection of the smallest value out of the set. One choice for A1 may be0.5, but other choices may be used. By way of another example, thechoice, A1=0.7 may also be used. Again, the constant A1 may beconsidered as a parameter of the model, which may be determined.

[0052] As another aggregation rule, by way of example, if very many ofthe rules have partial degrees of satisfaction of 0.9, then too muchadjusting may be occurring, and the applicant may be ruled out of cat1,even though the aggregation rule, MIN({DS−cat1})<=A1, may not besatisfied. The missing score (MS) is determined from the degree ofsatisfaction (DS) by MS=1−DS. If a given fuzzy rule has DS=0.9, then itwould have a missing score of 0.1. The aggregation criterion for thiscase might take the form, “reject from cat1 if SUM({MS−cat1})>=A2,”where A2 is a different chosen constant, the notation, SUM( . . . )denotes summation of all the elements of the set, and {MS−cat1} is theset of “missing scores” for each rule. The aggregation criteria abovemay use the sum of all of the missing scores for the cat1 rules as ameasure to determine when too much adjusting has been done, comparingthat with the constant A2. The measure defined above (SUM{MS−cat1}) maybe interpreted as a measure proportional to the difference between thedegree of complete satisfaction of all rules and the average degree ofsatisfaction of each rule (DS−cat1). It is understood in this inventionthat there may be any number of different kinds of aggregation criteria,of which the above two are only specific examples.

[0053] In a further step, the results of applying the aggregationcriteria to the set of rules relating to each category may be compared.A result according to one example may be that the applicant is ruled outof cat1 and cat2, but not from cat3 or cat4. In that case, assuming thatthe insurance company's policy was to place applicants in the bestpossible risk category, the final decision would be to place theapplicant in cat3. Other results may also be obtained.

[0054] As stated above, this fuzzy logic system may have many parametersthat may be freely chosen. It should be noted that the fuzzy logicsystem may extend and therefore subsume a conventional (Boolean) logicsystem. By setting the fuzzy logic system parameters to have only crispthresholds (in which the core value is equal to the support) the Booleanrules may be represented as a case of fuzzy rules. Those parameters maybe fit to reproduce a given set of decisions, or set by management inorder to achieve certain results. By way of one example, a large set ofcases may be provided by the insurance company as a standard to bereproduced as closely as possible. Preferably in such an example, theremay be many cases, thereby minimizing the error between the fuzzy rulesmodel and the supplied cases. Optimization techniques such as logisticregression, genetic algorithms, Monte Carlo, etc., also may be used tofind an optimal set of parameters. By way of another example, some ofthe fuzzy rules may be determined directly by the management of theinsurance company. This may be done through knowledge engineeringsessions with experienced underwriters, by actuaries acting onstatistical information related to the risk being insured or by othermanners. In fact, when considering maintenance of the system, initialparameters may be chosen using optimization versus a set of cases, whileat a future time, as actuarial knowledge changes, these facts may beused to directly adjust the parameters of the fuzzy rules. New fuzzyrules may be added, or aggregation rules may change. The fuzzy logicsystem can be kept current, allowing the insurance company to implementchanges quickly and with zero variability, thereby providing a processand system that is flexible.

[0055] According to one embodiment of the invention, the fuzzy logicparameters may be entered into a spreadsheet to evaluate the fuzzy rulesfor one case at a time. This may be essentially equivalent toimplementation in a manual processing type environment. FIG. 2 is agraphical representation illustrating a plurality of measurements basedon a degree of satisfaction for a rule. A graphical user interface (GUI)200 displays the degree of satisfaction for one or more rules. GUI 200includes a standard toolbar 202, which may enable a user to manipulatethe information in known manners (e.g., printing, cutting, copying,pasting, etc.). According to an embodiment of the invention, GUI may bepresented over a network using a browser application such as InternetExplorer®, Netscape Navigator®, etc. An address bar 204 may enable theuser to indicate what portion is displayed. A chart 206 displays variousinsurance decision components and how each insurance decision componentsatisfies its associated rule. A plurality of columns 208 illustrates aplurality of categories for each decision component, as well as aplurality of parameters for each decision component. A column 210identifies the actual parameters of the potential applicant forinsurance and a plurality of columns 212 illustrate a degree ofsatisfaction of each rule. By way of example, a row 214 is labeled BP(Sys), corresponding to a systolic blood pressure rule. To receive theBest or Preferred category classification, the applicant must have asystolic blood pressure score (score) between 140 and 150. To receive aSelect category classification, the applicant must have a score between150 and 155, while a score of 155 or more receives a “Standard Plus” orSt. Plus category classification. In this example, the applicant has ascore of 151. The columns 212 show zero satisfaction of the rule for theBest and Preferred category classifications. Additionally, FIG. 2 showsthat the applicant slightly missed satisfaction for the Select category,and Perfect Constraint Satisfaction for the St. Plus Category.

[0056] In another example, a row 216 is labeled BP (Dia.), correspondingto a diastolic blood pressure rule. To receive a Best categoryclassification, the applicant must have a diastolic blood pressure score(score) between 85 and 90, between 90 and 95 for a Preferred categoryclassification, between 90 and 95 for the Select categoryclassification, and between 95 and 100 for the St. Plus categoryclassification. Here, the applicant has a score of 70, resulting inPerfect Constraint Satisfaction in all of the columns 212.

[0057] By way of a further example, a row 218 is labeled Nicotine, wherea score between 4 and 5 receives the Best category classification, ascore between 2.5 and 3 receives the Preferred category classification,a score between 1.5 and 2 receives the Select category classification,and a score between 0.7 and 1 receives the St. Plus categoryclassification. In this example, the applicant has a score of 4.2. Thus,a score of “Mostly Missing” is indicated under the Best category of acolumn 212, while a score of Perfect Constraint Satisfaction isindicated for all others.

[0058] GUI 200 presents a submit button 220 to enable the user to accepta decision and submit it to a database. Alternatively, the user maydecide not to accept the decision. The user may activate a next button222 to record his/her decision. Other methods for display may also beused.

[0059] According to another embodiment of the invention, the rules maybe encoded into a Java-based computer code, which can query a databaseto obtain the case parameters, and write its decision in the database aswell. The object model of the java implementation is illustrated in FIG.3. This java implementation may be suitable for batch processing, or foruse in a fully automated underwriting environment. According to anembodiment of the invention, a rule engine (class RuleEngine) 302 may bethe control of the system. The decision components of rule engine 302may be composed of several rules (class Rule) 304, several aggregations(class Aggregation) 306 and zero or one decision post-processors (classDecisionPost-Processor) 308. A Rule object 304 may represent the fuzzylogic for one or a group of variables. Each rule is further composed ofa number of rateclasses (class Rateclass) 310. A Rateclass object 310defines the rules for a specific rateclass. According to an embodimentof the invention, a Rateclass object 310 may comprise two parts. Thefirst is pre-processing (class Preprocessor) 312, which may processmultiple inputs to form one output. The second is post-processing (classPostprocessor) 314, which may take the result of the pre-processing,feed it to a fuzzy function and get a fuzzy score. Some of the rules maybe conditional, such as the variable blood pressure systolic, where thethresholds vary depending on the age of the applicant. Class Condition316 may represent such a condition, if there is any. Classes FixedScore318, Minimal and Maximal may define some special preprocessingfunctions, and class Linear 320 may define the general linear fuzzyfunction as illustrated in FIG. 1.

[0060] According to an embodiment of the invention, there may be twophases at runtime for rule engine 302. The first phase may beinitialization. In the process, the rule definition file in XML formatconfigures the rule engine. All the rule engine parameters are definedin the process, for example, number of rules, the fuzzy thresholds, preand post processing and aggregation operation (including classIntersection 322 and Sum Missing 324) and class ThresholdLevel 326. Thesecond phase may be scoring. After correct initialization, thefireEngine method in rule engine 302 may take an input parameter—aninstance of class Case 328 containing all the required variable values,and output an instance of class Result 330, which encapsulates all thedecision results, including rateclass placement, the fuzzy scores foreach variable and each rateclass, and the aggregation scores. ClassResultLogger 332 may log the output. Other object models for a javaimplementation may also be used.

[0061]FIG. 4 is a flowchart illustrating the steps performed in aprocess for underwriting an insurance application using fuzzy logicrules according to an embodiment of the invention. At step 400, arequest to underwrite an insurance application may be received. Therequest to underwrite may come directly from a consumer (e.g., theperson being insured), an insurance agent or another person. The requestto underwrite comprises information about one or more components of theinsurance application. According to an embodiment of the invention, thecomponents may include the various characteristics associated with theindividual to be insured, such as a cholesterol level, a blood pressurelevel, a pulse, and other characteristics.

[0062] At step 410, at least one decision component is evaluated. Asdescribed above, evaluating a decision component may comprise evaluatinga decision component using a fuzzy logic rule. To perform theevaluation, a rule may be defined and assigned to the decisioncomponent. While each rule is generally only assigned one decisioncomponent, it is understood that more than one decision component may beassigned to each rule. Further, parameters for each rule may be defined,as also described above.

[0063] At step 420, at least one measurement is assigned to the at leastone decision component. As described above with regard to theapplication of a fuzzy logic rule, a measurement may be assigned to thedecision component from a sliding scale, such as between zero (0) andone (1). Other types of measurements may also be assigned.

[0064] At step 430, each decision component is assigned a specificcomponent category based on the assigned measurement. As describedabove, a number of specific component categories are defined. Based onthe assigned measurements, each decision component is assigned to one ormore specific component categories. By way of the examples above, thespecific component categories may be defined as cat1, cat2, cat3, andcat4. Cat1 may only be assigned decision components at a certain levelor higher. Similarly, cat2 may only be assigned decision components at asecond level or higher and so on. Other methods for assigning a specificcomponent category may also be used.

[0065] At step 440, the insurance application is assigned to a category.According to an embodiment of the invention, the categories to which theinsurance application is assigned are the same as the categories towhich the insurance decision components are assigned. As describedabove, the insurance application may be assigned to a category basedupon how the decision components were assigned. Thus, by way of example,an insurance application may be assigned to cat1 only if two or fewerdecision components are assigned to cat2 and all other decisioncomponents are assigned to cat1. Other methods for assigning aninsurance application to a category may also be used.

[0066] At step 450, an insurance policy is issued. Based on the categoryto which it is assigned, certain amounts are paid to maintain theinsurance policy in a manner that is well known in the industry. It isunderstood that based on a category, an insurance policy may not beissued. The customers may decide the premiums are too high.Alternatively, the insurance company may determine that the risk is toogreat, and decide not to issue the insurance policy.

[0067] Case Based Reasoning

[0068] A rule-based reasoning (RBR) system may provide for anunderwriting process by following a generative approach, typically arule-chaining approach, in which a deductive path is created from theevidence (facts) to the decisions (goals). A case-based reasoning (CBR)system, on the other hand, may follow an analogical approach rather thana deductive approach. In such a system, a reasoner may determine thecorrect rate class suitable for underwriting by noticing a similarity ofan application for insurance with one or more previously underwritteninsurance applications and by adapting known solutions of suchpreviously underwritten insurance applications instead of developing asolution from scratch. A plurality of underwriting descriptions andtheir solutions are stored in a CBR Case Base and are the basis formeasurement of the CBR performance. According to an embodiment of theinvention, a CBR system may be only as good as the cases within its CaseBase (also referred to as “CB”) and its ability to retrieve the mostrelevant cases in response to a new situation.

[0069] A case-based reasoning system can provide an alternative to arules-based expert system, and may be especially appropriate when anumber of rules needed to capture an expert's knowledge is unmanageable,when a domain theory is too weak or incomplete, or when such domaintheory is too dynamic. The CBR system has been successful in areas whereindividual cases or precedents govern the decision-making processes.

[0070] In many aspects, a case-based reasoning system and process is aproblem solving method different from other artificial intelligenceapproaches. In particular, instead of using only general domaindependent heuristic knowledge, such as in the case of an expert system,specific knowledge of concrete, previously experienced, problemsituations may be used with CBR. Another important characteristic may bethat CBR implies incremental learning, as a new experience is memorizedand available for future problem solving each time a problem is solved.CBR may involve solving new problems by identifying and adaptingsolutions to similar problems stored in a library of past experiences.

[0071] According to an embodiment of the invention, an inference cycleof the CBR process may comprise a plurality of steps, as illustrated inthe flow chart of FIG. 5. At step 502, probing and retrieving one ormore relevant cases from a case library is performed. Ranking theretrieved relevant cases, based on a similarity measure occurs at step504. At step 506, one or more best cases are selected. At step 508, oneor more retrieved relevant cases are adapted to a current case. Theretrieved, relevant cases are evaluated versus the current case, basedon a confidence factor at step 510. The newly solved case is stored inthe case memory at step 512.

[0072] These steps will be illustrated below within the context ofinsurance underwriting. However, one of ordinary skill in the art willrecognize that these steps may be used in other contexts as well. Forpurposes of this example only, assume that an applicant provides his/hervital sign information (e.g., an age, a weight, a height, a systolicblood pressure level and a diastolic blood pressure level, a cholesterollevel and a ratio, etc.) as a vector equal to:

X=[x ₁ , x ₂ . . . , x _(n)].

[0073] Furthermore, in this example, assume that two of the valuescorresponding to the cholesterol level, and a weight-to-height ratio,are above normal levels, while the others fall within normal ranges. Thefirst two components of vector X correspond to the cholesterol level(x₁) and the weight-to-height ratio (x₂). For purposes of this example,the applicant has an abnormally high cholesterol ratio (8.5%) and isover-weight (weight-to-height ratio=3.8 lb/inch). Furthermore, theapplicant has one medical condition/history, for instance a history ofhypertension. This condition may require the applicant to provideadditional detailed information related to the history of hypertension,e.g., a cardiomegaly, a chest pain, a blood pressure mean and a trendover the past three months (where mean is the average of the bloodpressure readings over a particular time period and trend corresponds tothe slope of the reading such as upward, or downward, etc.) The detailedinformation may be contained in a vector Y=[y₁, y₂, . . . , y_(p)],where the value of p will vary according to the applicant's medicalcondition.

[0074] The first step in the CBR methodology may be to represent a newcase (probe) as a query in a structured query language (SQL), which maybe formulated against a database of previously placed applicants(cases). According to an embodiment of the invention, the SQL query maybe of the form:

Q:[f ₁(x), f ₂(x), . . . , f _(n)(x)]AND[Condition=label]

[0075] where [f₁(x), f₂(x), . . . , f_(n)(x)], will be a vector of nfuzzy preference functions, one of each of the elements of vector X, anda label will be an index representing the applicant's current medicalcondition. For this example, the CBR system may retrieve all previousapplicants with a history of hypertension, whose vital signs werenormal, except for a cholesterol ratio and a weight-to-height ratio. Inother words, the SQL query may be for all cases matching the samecondition and similar vital information as the applicant. An example ofsuch a SQL query may be:

Q1=[Support(Around(8.5%;x)), Support(Around(3.8;x)), Support(Normal(i)),. . . , Support(Normal(n))]AND[Condition=Hypertension]

[0076] The meaning of Normal(i) may be determined by a fuzzy logic setrepresenting a soft threshold for a variable, x(i), as it is used in thestricter class rate, (e.g., Preferred Best in the case of LifeInsurance.) FIG. 6 illustrates the case of Normal (j), where x(j)corresponds to the cholesterol ratio. For example, it may be determinedfrom the most experienced underwriters of the insurance company thatunder no circumstances can the applicant get the best class rate ifhis/her cholesterol ratio is above X1 by more than five points. In thatexample, one may use X1b−X1a=5. The specific values for X1a and X1b maybe parameters of the model, and will be explained below in greaterdetail. To obtain the partial degree of satisfaction when thecholesterol ratio value falls within the range [X1a, X1b], a continuousswitching function may be used which interpolates between the values 1and 0. The simplest such function is a straight line, but otherfunctions may also be used.

[0077] In a linear membership function as shown in FIG. 6, the valuesX1a and X1b are the low and high cutoffs, respectively. A strict yes/norule may be recovered in the limit that X1a=X1b. Thus, many methods thatmix fuzzy and strict rules in any proportion may be covered as a subsetof this method.

[0078] Around (a; x) may be determined by a fuzzy relationship, whosemembership function can be interpreted as the degree to which the valuex meets the property of “being around a.” If Around (a; x)=1, then thevalue of x may be close to a well within a desired tolerance. Thesupport of the fuzzy relationship Around (a; x) may be defined as theinterval of values of x for which Around (a; x)>0, as illustrated inFIG. 7. If Around (a; x)=0 then the value of x is too far from a, beyondany acceptable tolerance.

[0079] The core of the fuzzy relationship Around (a; x) may be definedas the interval of values of x for which Around (a; x)=1, as illustratedin FIG. 8. Any value belonging to the core fully satisfies the propertyand, in terms of a preference, it is indistinguishable from any othervalue in the core.

[0080] A trapezoidal membership distribution representing therelationship may have a natural preference interpretation. The supportof the distribution may represent a range of tolerable values andcorrespond to an interval-value used in an initial SQL retrieval query.The core may represent the most desirable range of values and mayestablish a top preference. By definition, a feature value fallinginside the core will receive a preference value of 1. As the featurevalue moves away from a most desirable range, its associated preferencevalue will decrease from 1 to 0. By retrieving the cases havingcholesterol ratios falling in the support of Around (8.5%; x) and havingweight-to-height ratios falling in the support of Around (3.8; x) allpossible relevant cases may be retrieved.

[0081] In executing an SQL query Q1 of the above example against the CBRdatabase, N cases may be retrieved. By construction, all N cases musthave all of their vital values inside the support of the correspondingelement x(i) defined by Q1. Furthermore, all cases must be related tothe same medical condition, (e.g., hypertension).

[0082] At this point, considering the outputs of each of the N retrievedcases may provide a first preliminary decision. According to anembodiment of the invention, a decision may be made only on theretrieved cases, i.e., only using the first n variables and the labelused in the SQL query Q1. Each retrieved case may be referred to as acase C_(k) (k between 1 and N), and an output classification of caseC_(k) as O_(k), where O_(k) is a variable having an attribute valueindicating the rate class assigned to the applicant corresponding tocase C_(k). By way of example, O_(k) may assume one out of T possiblevalues, i.e., O_(k)=L, where Lε{R₁, R₂, . . . ,R_(T)}. For instance, inthe case of Life insurance products, L={Preferred-Best, Preferred,Preferred-Nicotine, . . . , Standard, . . . , Table-32}. Other valuesmay also be used.

[0083] In this example, the SQL query Q1 retrieves 40 cases (N=40). FIG.9 illustrates the histogram (distribution of the retrieved cases overthe rate classes) of the results of the SQL query Q1. As seen in FIG. 9,a first preliminary decision indicates Table-II as being the most likelyrate class for the new applicant represented by the SQL query Q1.

[0084] All N cases may have all their vital values inside the support ofthe corresponding element x(i) defined by the SQL query Q1 and they areall related to the same medical condition, (e.g., hypertension).Therefore, each case may also contain p additional elementscorresponding to the variables specific to the medical condition. A caseC_(k) (k between 1 and N) may be represented as an r-dimensional vector,where r=n+p. The first n elements correspond to the n vital signdescribed by the vector X, namely [x_(1,k), x_(2,k), . . . ,x_(n,k)].The remaining p elements may correspond to the specific features relatedto the condition hypertension, namely [x_((n+1),k), X_((n+2),k), . . . ,x_(r,k)]. The value of p may vary according to the value of the label,i.e., the medical condition.

[0085] A degree of matching between case C_(k) and the SQL query Q1 maybe determined. To this extent, the n-dimensional vector M(C_(k), Q1) maybe defined as an evaluation of each of the functions [f₁(x), f₂(x), . .. , f_(n)(x)] from the SQL Query Q1 with the first n elements of C_(k),namely [x_(1,k), x_(2,k), . . . , x_(n,k)]:

M(C _(k) , Q1)=[f ₁(x _(1,k)), f ₂(x _(2,k)), . . . , f _(n)(x _(n,k))]

[0086] At the end of this evaluation, each case will have a preferencevector whose elements take values in the (0,1] interval (where thenotation (0,1] indicates that this is an open interval at 0 (i.e., itdoes not include the value 0), and a closed interval at 1 (i.e., itincludes the value 1)). These values may represent a partial degree ofmembership of the feature value in each case and the fuzzy relationshipsrepresenting preference criteria in the SQL query Q1. Since thispreference vector represents a partial order, the CBR system aggregatesits elements to generate a ranking of the case, according to theiroverall preference.

[0087] A determination is made of an n-dimensional weight vector W=[w₁,w₂, . . . , w_(n)] in which the element w_(i) takes a value in theinterval [0,1] and determines the relative importance of feature i inM(C_(k), Q1), i.e., the relevance of f_(i) (x_(i,k)). According to anembodiment of the invention, this can be done via direct elicitationfrom an underwriter or using pair-wise comparisons, following Saaty'smethod. By way of example, if all features are equally important, alltheir corresponding weights may be equal to 1. Other methods may also beused. Once the weight vector has been determined, several aggregatingfunctions are used to rank the cases, where the aggregating functionwill map an n-dimensional unitary hypercube into a one-dimensional unitinterval, i.e.,: [0,1]^(n)−>[0,1].

[0088] To consider compensation among the elements, a definition is madeof the aggregating function A[W,M(C_(k), Q1)] as a weighted sum of itselements, i.e.:${A\left\lbrack {W,{M\left( {C_{k},{Q1}} \right)}} \right\rbrack} = {\sum\limits_{i = 1}^{n}\quad {w_{i}{f_{i}\left( x_{i,k} \right)}}}$

[0089] Alternatively, a strict intersection aggregation withoutcompensation may be obtained using a weighted minimum, i.e.:

A[W,M(C _(k) , Q1)]=Minimum_(1, . . . ,n)[max(1−w _(i)), f(x _(i,k))]

[0090] Regardless of the aggregating function selected, it may beconsidered as a measure of similarity between the each retrieved caseC_(k) and the query Q1, and may be referred to as S(k,1). Using thismeasure, cases may be sorted according to an overall degree ofpreference, which may be interpreted as a measure of similarity betweeneach retrieved case C_(k) and the query Q1.

[0091] In the first preliminary decision, the output of case C_(k) maybe referred to as O_(k), where O_(k) is a variable whose attribute valueindicates a rate class assigned to the applicant corresponding to a caseC_(k). Assume, for example, that O_(k) can take one out of T possiblevalues, i.e., O_(k)=L, where Lε{R₁, R₂, . . . , R_(T)}. For instance, inthe case of Life insurance products, L={Preferred-Best, Preferred,Preferred-Nicotine, . . . , Standard, . . . , Table-32}. However, notall cases are equally similar to our probe. FIG. 10 illustrates adistribution of the similarity measure S(k,1) over the T for theretrieved N cases (e.g., N=40 in the present example).

[0092] According to an embodiment of the invention, a minimum similarityvalue may be considered for a case. For instance, to only considersimilar cases, a threshold may be established on the similarity value.By way of example, only cases with a similarity greater or equal to 0.5may be considered. According to an embodiment of the invention, adetermination may be made of a fuzzy cardinality of each of the rateclasses, by adding up the similarity values in each class. Otherdistributions may also be evaluated.

[0093] A histogram may be drawn that aggregates the original retrievalfrequency with the similarity of the retrieved cases, and may bereferred to as a pseudo-histogram. This process may be similar to aN-Nearest Neighbor approach, where the N retrieved cases represent the Npoints in the neighborhood, and the value of S(k,1) represents thecomplement of the distance between the point K and the probe, i.e., thesimilarity between each case and a query. The rate class Ri, with thelargest cumulative measure may be proposed as a solution. By way ofexample, Table-II is the solution indicated by either option.

[0094] A decision may be made on how many cases will be used to refine asolution. Having sorted the cases along the first n dimensions, theremaining p dimensions may be analyzed corresponding to the featuresrelated to the specific medical condition. Some of these medicalconditions may have variables with binary or attribute values (e.g.,chest pain (YIN), malignant hypertension (N), Mild, Treated, etc.),while others ones may have continuous values (e.g., cardiomegaly (% ofenlargement), systolic and diastolic blood pressure averaged and trendin past 3 months, 24 months, etc.).

[0095] An attribute-value and a binary-value may be used to select,among the N retrieved cases, the cases that have the same values. Thismay be the same as performing a second SQL query, thereby refining thefirst SQL query Q1. From the originally retrieved N cases, the caseswith the correct binary or attribute values may be selected. This may bedone for all of the attribute-values and the binary-valued variables, orfor a subset of the most important variables. After this selection, theoriginal set of cases will likely have been reduced. However, when aCase Base is not sufficiently large, a reduction in the number ofvariables used to perform this selection may be needed. Assuming thatthere are now L cases (where L<N), these cases may still be sortedaccording to a value of a similarity metric S(k,1).

[0096] A third preliminary decision may be obtained by re-computing thedistribution of the similarity measure S(k,1) over the T values for theoutput O_(k), and then proposing as a solution the class Ri with thelargest cumulative measure using the same pseudo-histogram methoddescribed above.

[0097] A similarity measure over the numerical features related to themedical condition may be obtained by establishing a fuzzy relationshipAround(a; x) similar to the one described above. This fuzzy relationshipwould establish a neighborhood of cases with similar conditionintensities. By performing an evaluation and an aggregation similar toone described above, a similarity measure may be obtained by medicalcondition, and may be referred to as I(k,1).

[0098] A final decision may involve creating a linear combination ofboth similarity measures:

F(k,1)=αS(k,1)+(1−α)I(k,1),

[0099] thereby providing the distribution of the final similaritymeasure F(k,1) over the T values of O_(k). According to an embodiment ofthe invention, the final decision or solution may be the class R_(i)with the largest cumulative measure using the same pseudo-histogrammethod.

[0100] A reliability of the solution may be measured in several ways,and as a function of many internal parameters computed during thisprocess. According to an embodiment of the invention, the number ofretrieved (N) and refined (L) cases (e.g., area of the histogram) may bemeasured. Larger values of N+L may imply a higher reliability of thesolution. According to another embodiment of the invention, the fuzzycardinality of the retrieved and refined cases (i.e., area of thepseudo-histogram) may be measured. Larger values may imply a higherreliability of the solution. According to a further embodiment of theinvention, the shape of the pseudo-histogram of the values of O_(k),(i.e., spread of the histogram) may be measured, where a tighterdistribution (smaller sigmas) would be more reliable than scatteredones. According to another embodiment of the invention, the mode of thepseudo-histogram of the values of O_(k), (e.g., maximum value of thehistogram) may be measured. Higher values of the mode may be morereliable than lower ones. A contribution of one or more of thesemeasurements may be used to determine reliability. Other measurementsmay also be used.

[0101] Using a training set, a conditional probability ofmisclassification as a function of each of the above parameters may bedetermined, as well. Then, the (fuzzy) ranges of those parameters may bedetermined and a confidence factor may be computed.

[0102] If the solution does not pass a confidence threshold (e.g.,because it does not have enough retrieved cases, has a scatteredpseudo-histogram, etc.), then the CBR system may suggest a solution tothe individual underwriter and delegate to him/her the final decision.Alternatively, if the confidence factor is above the confidencethreshold, then the CBR system may validate the underwriter's decision.Regardless of the decision maker, once the decision is made, the newcase and its corresponding solution are stored in the Case Base,becoming available for new queries.

[0103] According to an embodiment of the invention, clean cases(previously placed by rule base) may be used to tune the CBR parameters(e.g., membership functions, weights, and similarity metrics), therebyabating risk. Other methods for abating risk may also be used.

[0104] By defining and using three stages of preliminary decisions, theCBR system may display tests, thereby generating useful information forthe underwriter while the Case Base is still under development. As moreinformation (cases and variables describing each case) is stored in theCase Base, the CBR system may be able to use a more specific decisionstage.

[0105] According to an embodiment of the invention, the first twopreliminary decision stages may only require the same vital informationused for clean applications and the symbolic (i.e., label) informationof the medical condition. A third decision stage may make use of asubset of the variables describing the medical condition therebyrefining the most similar cases. The subset of variables may be chosenby an expert underwriter as a function of their relevance to the insuredrisk (mortality, morbidity, etc.). This step will allow the CBR systemto refine the set of N retrieved cases, and select the most similar Lcases, on the basis of the most important binary and attribute variablesdescribing the medical condition. The final two preliminary decisionstages may only require the same vital information used for cleanapplications and the symbolic (i.e., label) information of the medicalcondition.

[0106] According to an embodiment of the invention, it may be importantthat at all times the value of N (for the first two decision stages) andthe value of L (for the third decision stage) be large enough to ensuresignificance. The number of cases used may be one of the parameters usedto compute the confidence factor described above.

[0107] In the first step of the example, the new case (probe) wasrepresented as a SQL query, and it was assumed that only one medicalcondition was present. The complete SQL query Q may have been formulatedas:

Q:[f ₁(x), f ₂(x), . . . , f _(n)(x)]AND[Condition=label]AND[Conditionnumber=1]

[0108] If the applicant has more than one medical condition, theapplicant may be compared with other applicants having the same medicalconditions. By way of another example extending the original exampleused, the applicant is assumed to have an abnormally high cholesterolratio (8.5%) and be over-weight (weight-to-height ratio=3.8 lb/inch).Furthermore, the applicant discloses that he/she has two medicalconditions, (e.g., hypertension and diabetes).

[0109] In a densely populated Case Base, the applicant may berepresented by the query:

Q:[f ₁(x), f ₂(x), . . . , f _(n)(x)]AND[Condition 1=label]AND[Condition2=label 2]AND[Condition number=2]

[0110] This query may be instantiated as:

Q1:[Support(Around(8.5%,x)), Support(Around(3.8;x)), Support(Normal(i)),. . . ,Support(Normal(n))]AND[Condition=Hypertension]AND[Condition=Diabetes]AND[Conditionnumber=2]

[0111] With a well-populated Case Base, this may be a process forhandling multiple medical conditions in complex cases.

[0112] As more conditions are added to a query, fewer cases will likelybe retrieved. If the retrieved number of cases N is not significant, auseful decision may not be produced. An alternative (surrogate) solutionmay be to decompose a query into two separate queries, treating eachmedical condition separately. For instance, assuming that the modifiedquery Q1 requesting two simultaneous conditions does not yield anymeaningful result, the CBR system may decompose the query Q1 into aplurality of queries, Q1-A and Q1-B:

where Q1-A:[Support(Around(8.5%,x)), Support(Around(3.8;x)),Support(Normal(i)), . . . ,Support(Normal(n))]AND[Condition=Hypertension]AND[Condition number=1];and

where Q1-B:[Support(Around(8.5%,x)), Support(Around(3.8;x)),Support(Normal(i)), . . . ,Support(Normal(n))]AND[Condition=Diabetes]AND[Condition number=1]

[0113] Each query may be treated separately and may obtain a decision onthe rate class for each of the queries. In other words, it may beassumed that there are two applicants, both overweight and with a highcholesterol ratio, one with hypertension and one with diabetes.

[0114] After obtaining suggested placements in the appropriate rateclass, (e.g., RC-A and RC-B, respectively) the answers may be combinedaccording to a set of aggregation rules representing the union ofmultiple rate classes induced by the presence of multiple medicalconditions. According to an embodiment of the invention, these rules maybe elicited from experienced underwriters. A look-up table, asillustrated in FIG. 11, may represent this rule set. FIG. 11 is just anexample that shows a linear aggregation of the rate classes. Assume thatthe rate class assigned to query Q1-A is RC-A=Table 6 and the rate classassigned to query Q1-B is RC-B=Table 8. The combined rate classgenerated from the aggregation rule is RC=Table 14. Other tables may bedesigned to over-penalize the occurrence of multiple conditions as theirpresence might affect risk and, therefore, claims, in a non-linearfashion. For example RC-A=Table 6 and RC-B=Table 8 could be aggregatedinto RC=Table 18 by a stricter table. Other aggregation process may alsobe used.

[0115] Additionally, these tables may be used in an associative fashion.In other words, when an applicant has three or more medical conditions,the CBR system may aggregate the rate classes derived from the first twomedical conditions, obtain the result and aggregate the result with therate class obtained from the third medical condition, and so on, asillustrated in FIG. 11. This method is a surrogate alternative that maybe used when enough cases with multiple conditions are included in theCase Base.

[0116] According to an embodiment of the invention, a CBR engine may beencoded into a Java based computer code, which can query a database toobtain the case parameters, and write its decision in the database aswell. This embodiment may be suitable for batch processing, and for usein a fully automated underwriting environment.

[0117] Calculation of Confidence Factor

[0118] A described above, CBR may be used to automate decisions in avariety of circumstances, such as, but not limited to, business,commercial, and manufacturing processes. Specifically, it may provide amethod and system to determine at run-time a degree of confidenceassociated with the output of a Case Based Decision Engine, alsoreferred to as CBE. Such a confidence measure may enable a determinationto be made on when a CBE decision is trustworthy enough to automate itsexecution and when the CBE decision is not as reliable and may needfurther consideration. If a CBE decision is not determined to be asreliable, a CBE analysis may still be beneficial by providing anindicator, forwarding it to a human decision maker, and improving thehuman decision maker's productivity with an initial screening that maylimit the complexity of the final decision. The run-time assessment ofthe confidence measure may enable the routing mechanism and increasesthe usefulness of a CBE.

[0119] An embodiment of the invention may comprise two parts: a) therun-time computation of a confidence factor for a query; and b) thedetermination of the threshold to be used with the computed confidencefactor. FIG. 12 is a flowchart illustrating a process for determining arun-time computation of a confidence factor according to an embodimentof the invention. At Step 1200, a confidence factor process isinitiated. At Step 1210, CBE internal parameters that may affect theprobability of misclassification are identified. At Step 1220, theconditional probability of misclassification for each of the identifiedparameters is estimated. At Step 1230, the conditional probability ofmisclassification is translated into a soft constraint for eachparameter. At Step 1240, a run-time function to evaluate the confidencefactor for each new query is defined. The determination of the thresholdfor the confidence factor may be obtained by using a gradient-basedsearch. It is understood that other steps may be performed within thisprocess, and/or the order of steps may be changed. The process of FIG.12 will now be described in greater detail below.

[0120] According to an embodiment of the invention, CBE may be used toautomate the underwriting process of insurance policies. By way ofexample, CBE may be used for underwriting life insurance applications,as illustrated below. It is understood, however, that the applicabilityof this invention is much broader, as it may apply to any Case-BasedDecision Engine(s).

[0121] According to an embodiment of the invention, an advantage of thepresent invention may include improving deployment of a method andsystem of automated insurance underwriting, based on the analysis ofprevious similar cases, as it may allow for an incremental deployment ofthe CBE, instead of postponing deployment until an entire case base hasbeen completely populated. Further, a determination may be made forwhich applications (e.g., characterized by specific medical conditions)the CBE can provide sufficiently high confidence in the output to shiftits use from a human underwriter productivity tool to an automatedplacement tool. As a case base (also referred to as a “CB”) is augmentedand/or updated by new resolved applications, the quality of theretrieved cases may improve. Another advantage of the present inventionmay be that the quality of the case base may be monitored, therebyindicating the portion of the case base that requires growth orscrubbing. For instance, monitoring may enable identification of regionsin the CB with insufficient coverage (small area histograms, lowsimilarity levels), regions containing inconsistent decisions (bimodalhistograms), and ambiguous regions (very broad histograms).

[0122] In addition, by establishing a confidence threshold, adetermination may be made whether the output can be used directly toplace the application or if it will be a suggestion to be revised by thehuman underwriter, where such a determination may be made for eachapplication processed by the CBE. Further, according to an embodiment ofthe invention, a process may be used after the deployment of the CBE, aspart of maintenance of the case base. As the case base is enriched bythe influx of new cases, the distribution of its cases may also vary.Regions of the case base that were sparsely populated might now containa larger number of cases. Therefore, as part of the tuning of the CBE,one may periodically recompute certain steps within the process toupdate the soft constraints on each of the parameters. As part of thesame maintenance, one may also periodically update the value of the bestthreshold to be used in the process.

[0123] While the present invention is described in relation toapplicability to the improvement of the performance of a Case BasedEngine for Digital Underwriting, it is understood that the method andsystem described herein may be applied to any Case Based Reasoningsystem, to annotate the quality of its output and decide whether or notto act upon the generated output. By way of example, CBR systems mayhave applications in manufacturing, scheduling, design, diagnosis,planning, and other areas.

[0124] As described above, the CBE relies on having a densely populatedCase Base (“CB”) from which to retrieve the precedents for the newapplication (i.e., the similar cases). According to an embodiment of theinvention, until the CB contains a sufficiently large number of casesfor most possible applications, the CBE output may not be reliable. Suchan output may, by way of example, be used as a productivity aid for ahuman underwriter, rather than an automation tool.

[0125] For each processed application, a measure of confidence in theCBE output is computed so that a final decision maker (CBE or humanunderwriter) may be identified. As the decision engine generates itsoutput from the retrieval, selection, and adaptation of the most similarcases, such a confidence measure may reflect the quality of the matchbetween the input (the application under consideration) and the currentknowledge, e.g., the cases used by the CBE for its decision.

[0126] The confidence measure proposed by this invention needs toreflect the quality of the match between the current application underconsideration and the cases used for the CBE decision. This measureneeds to be evaluated within the context of the statistics formisclassification gathered from the training set. More specifically,according to an embodiment of the invention, the steps described belowmay be performed. These steps may include, but are not limited to, thefollowing: 1) Formulate a query against the CB, reflecting thecharacteristics of the new application as query constraints; 2) Retrievethe most relevant cases from the case library. For purposes ofillustration, assume that N cases have been retrieved, where N isgreater than 0 (i.e., not a null query or an empty retrieved set ofcases). A histogram of the N cases is generated over the universe oftheir responses, i.e., a frequency of the rate class; 3) Rank theretrieved cases using a similarity measure; 4) Select the best casesthereby reducing the total number of useful retrieved cases from N to L;and 5) Adapt the L refined solutions to the current case in order toderive a solution for the case. By way of example, selecting the mode ofthe histogram may be used to derive a solution.

[0127] To determine the confidence in the decision, it may be desirableto understand what the probability of generating a correct or incorrectclassification is. Specifically, it may be desirable to identify whichfactors affect misclassifications, and, for a given case, use thesefactors to assess if it is more or less likely to generate a wrongdecision. According to an embodiment of the invention, unless a decisionis binary, the decision will consist of placing the case underconsiderations in one of several bins. Hence, there may be differentdegrees of misclassification, depending on the distance of the CBEdecision from the correct value. Given the different costs associatedwith different degrees of misclassification, the factors impacting thedecision may be used with the likely degree of misclassification.

[0128] One aspect of the present invention deals with the process andmethod used to accomplish this result. At Step 1210 the CBE internalparameters that might affect the probability of misclassification may bedetermined. Each of these parameters may be referred to as an x.Furthermore, assume that there are M parameters (i.e., i=1, . . . M,forming a parameter vector X=[x₁, . . . x_(M)].

[0129] Parameters that may affect the probability of misclassificationinclude, but are not limited to, the following potential list ofcandidates:

[0130] x₁: N=Number of retrieved cases (i.e., cardinality of retrievedset and area of histogram in FIG. 9), e.g., N=40 cases.

[0131] x₂: variability of retrieved cases (measure of dispersion ofhistogram in FIG. 9).

[0132] x₃: number of retrieved cases thresholded by similarity value(area of histogram in FIG. 10) e.g., 25 cases.

[0133] x₄: variability of retrieved cases thresholded by similarityvalue. (measure of dispersion of histogram in FIG. 10).

[0134] x₅: L=number of refined cases. (i.e., cardinality of refined set)e.g., 21 cases.

[0135] x₆: variability of refined cases.

[0136] x₇: number of refined cases, thresholded by similarity valuee.g., 16 cases.

[0137] x₈: variability of refined cases thresholded by similarity value.

[0138] x₉: measure of strength of mode (percentage of cases in mode ofhistogram) e.g., 50%.

[0139] According to an embodiment of the invention, other parameters mayinclude:

[0140] x₁₀: number of retrieved cases weighted by similarities. (i.e.fuzzy cardinality of retrieved set (area of histogram in FIG. 9)).

[0141] x₁₁: variability of retrieved cases weighted by similarities(measure of dispersion of histogram in FIG. 9).

[0142] x₁₂: number of refined cases weighted by similarities(i.e. fuzzycardinality of refined set).

[0143] x₁₃: variability of refined cases weighted for similarities.

[0144] These parameters may be query-dependent, (e.g., they may vary foreach new application). This may be in contrast to static designparameters, such as, but not limited to, similarity weights, retrievalparameters, and confidence threshold. Static parameters may be tuned atdevelopment time (e.g., when a system is initially developed) andperiodically revised at maintenance time(s) (e.g., during maintenanceperiods for a system). According to an embodiment of the invention,static parameters may be considered fixed while evaluating parameters[x₁-x₉:].

[0145] According to an embodiment of the invention, the above parametersmay likely be positively correlated. By way of example, the number orrefined cases L may depend on the total number of cases N. The relativeimpact of these parameters may be evaluated via a statisticalcorrelation analysis, CART, C4.5 or other algorithms to identify andeliminate those parameters that contribute the least amount ofadditional information. By way of another example, methods may be usedto handle partially redundant information in a way that avoids doublecounting of the evidence. The use of a minimum operator in thecomputation of the Confidence Factor, as is described below, is such anexample.

[0146] According to an embodiment of the invention, at step 1220, theconditional probability of misclassification for each parameter x_(i)(for i=1 . . . 9) may be estimated. By way of example, this step may beachieved by running a set of experiments with a training set. Given acertified Case Base (e.g., a CB containing a number K of cases whoseassociated decisions were certified correct), the following steps maythen be followed:

[0147] (1) For each of the K cases in the CB, one case is selected (fromthe CB) and may be considered as the probe, i.e., the case whosedecision we want to determine (1310).

[0148] (2) The Case Based Engine (CBE) and the (K-1) cases remaining inthe CB may then be used to determine the rate class (i.e., the placementdecision for the probe) (1320).

[0149] (3) The decision derived from the CBE may then be compared withthe original certified decision of the probe (1330).

[0150] (4) The comparison and its associated parameters [x₁-x₉] may thenbe recorded.

[0151] (5) The selected case may be placed in the CB and another caseselected. (i.e., back to step (1) (1340)).

[0152] (6) Perform steps (2) through (5) until all the K cases in the CBhave been used as probes (1350).

[0153] This process is illustrated in FIG. 13. Once the process iscompleted, the results may be collected and analyzed. The comparisonmatrix of FIG. 14 illustrates a comparison between a probe's decisionderived from the CBE and the probe's certified reference decision. Thecells located on the comparison matrix's main diagonal may contain thepercentage of correct classifications. The cells off the main diagonalmay contain the percentage of incorrect classifications. As waspreviously mentioned, there may be different degrees ofmisclassification, depending on the distance of a CBE decision from thecorresponding reference decision.

[0154] At this point, it may be desirable to estimate the conditionalprobability of misclassification given each of parameters [x₁-x₉]. Sinceeach case in the comparison matrix has its associated parameters [x₁-x₉]recorded, a histogram of the distance from the correct decision for eachof these parameters may be generated. This process may be illustrated bya simple example. As was previously described, the value of the firstparameter x₁:

[0155] x₁: N=Number of retrieved cases. (i.e., cardinality of retrievedset (area of histogram in FIG. 9))

[0156]FIG. 15 shows an example of cross-tabulation of classificationdistances and number of retrieved cases for each probe. By way of thisexample, the processing of 573 probes is shown, achieving a correctclassification for 242 of them. Additionally, 214 were classified as onerate class off (where 114 at (−1) and 100 at (+1) equal 214). Further,99 were two rate classes off (where 64 at (−2) and 35 at (+2) equal 99),and 18 were 3 or more classes off. These 573 cases may also besubdivided in ten bins, representing ranges of the number of retrievedcases used for each probe. By way of example, 41 cases had between 1 and4 retrieved cases (first column), while 58 cases used more than 40retrieved cases (last column). FIG. 16 illustrates the samecross-tabulation using percentages instead of the number of cases.According to an embodiment of the invention, this table may be referredto as matrix D(i, j), where i=1 . . . 7 (the seven distancesconsidered), and j=1 . . . 10 (the ten bins considered).

[0157] Note that this table contains the same percentages illustrated inFIG. 15, once we normalize the values by the total number of cases,tabulated for different values of x₁. For instance, the total percentageof Correct Classifications (CC) in FIG. 14 may be defined as the sum ofthe elements on the main diagonal, i.e.:${\% \quad {CC}} = {\sum\limits_{i = 1}^{T}\quad {M\left( {i,i} \right)}}$

${\% \quad {CC}} = {\sum\limits_{i = 1}^{T}\quad {M\left( {i,i} \right)}}$

[0158] The same percentage may be obtained by adding the percentagesdistributed along the fourth row (corresponding to Distance 0), i.e.:$\begin{matrix}{{\% \quad {CC}} = {\sum\limits_{j = 1}^{10}\quad {D\left( {4,j} \right)}}} \\{{\% \quad {CC}} = {\sum\limits_{j = 1}^{10}\quad {D\left( {4,j} \right)}}}\end{matrix}$

[0159] The percentage of correct classification may increase with thenumber of cases retrieved for each probe (fourth row, distance=0). Byanalyzing a given column on this table, an estimate may be derived ofthe probability of correct/incorrect classification, given that thenumber of cases is in the range of values corresponding to the column.

[0160] According to an embodiment of the invention, step 1230 maycomprise translating the conditional probability of misclassificationinto a soft constraint for each parameter x_(i)(for i=1 . . . 9). By wayof example, all misclassifications are determined to be equallyundesirable, the only concern may be with the row corresponding todistance equal 0 (i.e., correct classification), as illustrated in FIG.17. By way of another example, it may be desirable to penalize morethose misclassifications that are two or three rate classes away fromthe correct decision. Therefore, an overall performance function may beformulated that aggregates the rewards of correct classifications withincreasing penalties for misclassifications. Although various types ofaggregating function may be used to achieve these ends, one possibleaggregating function may use a weighted sum of rewards and penalties.Specifically, for each bin (range of values) of the parameter x₁ underconsideration, a reward/penalty w_(i) may be considered. For instance:${f\left( {Bin}_{k} \right)} = {\sum\limits_{i = 1}^{7}\quad {w_{i}{D\left( {i,k} \right)}}}$

[0161] Where, for example, the weight vector W[w_(i)], i=1 . . . 7 isW=[−11, −6, −1, 4, −1, −6.−11]

[0162] This weight vector indicates that misclassifying a decision bythree or more rate classes is eleven times worse than amisclassification that is one rate class away. Except for the fourthelement, which indicates the reward for correct classifications, allother elements in vector W indicate the penalty value for thecorresponding degree of misclassification. FIG. 18 illustrates theresult of applying the performance function f(Bin_(k)) to the values ofFIG. 16, i.e., Matrix D.

[0163] By interpreting the values of FIG. 18 as degree of preference, afuzzy membership function Ci(x_(i)), is derived, indicating thetolerable and desirable ranges for each parameter x₁. According to anembodiment of the invention, a possible way to convert the values ofFIG. 18 to a fuzzy membership function is to replace any negative valuewith a zero and then normalize the elements by the largest value. Inthis example, the result of this process is illustrated in FIGS. 19 and20.

[0164] As previously described, the membership function of a fuzzy setis a mapping from the universe of discourse (the range of values of theperformance function) into the interval [0,1]. The membership functionhas a natural preference interpretation. The support of the membershipfunction Ci(x_(i)) represents the range of tolerable (i.e., acceptable)values of x_(i). The support of the fuzzy set Ci(xi) is defined as theinterval of values of x for which Ci(xi)>0. Similarly, the core mayrepresent the most desirable range of values and establish a toppreference. The core of the membership function Ci(x_(i)) may be definedas the interval of values x_(i), for which Ci(x_(i))=1. In the exampleof FIG. 20, the support is [22, infinity] and the core is [40,infinity]. By definition, a feature value falling inside the core willreceive a preference value of 1. As the feature value moves away fromthe most desirable range, its associated preference value will decreasefrom 1 to 0. At this point, the information may be translated into asoft constraint representing our preference for the values of parameterx_(i). The soft constraint may be referred to as Ci(x_(i)), asillustrated in FIG. 20.

[0165] According to an embodiment of the invention, a fourth step ofthis invention may be to define a run-time function to evaluate theconfidence measure for each new query. By way of example, afterexecuting the third step for each of the nine parameters, nine softconstraints may be obtained Ci(x_(i)) i=1, . . . , 9. A soft constraintevaluation (SCE) vector is generated that contains the degree to whicheach parameter satisfies its corresponding soft constraint; SCE [C₁(x₁),. . . , C₉ (x₉)]. The Confidence Factor (CF_(j)) to be associated toeach new case j may be computed at run-time as the intersection of allthe soft constraints evaluations contained in the SCE vector.${CF}_{j} = {{\underset{i = 1}{\bigcap\limits^{9}}{C_{i}\left( x_{i} \right)}} = {{Min}_{i = 1}^{9}{C_{i}\left( x_{i} \right)}}}$

[0166] According to an embodiment of the invention, all elements in theSoft Constraint Evaluation (SCE) vector may be real numbers in theinterval [0,1]. Therefore the Confidence Factor CF_(j) will also be areal number in the interval [0,1]. Nine potential soft constraintsrepresent the most desirable fuzzy ranges for the nine parametersdescribed above. Given a new probe, its computed parameter vectorX=[x₁-x₉] may used be to determine the degree to which all softconstraints are satisfied (SCE), leading to the computation of itsConfidence Factor CF.

[0167] As previously described above, a four-step process was describedto compute at run-time the confidence factor. The minimum threshold forthe confidence value may be determined by a series of experiments withthe data, to avoid being too restrictive or too inclusive. Ahigher-than-needed threshold may decrease the coverage provided by theCBE by rejecting too many correct solutions (False Negatives). As thethreshold is lowered, the number of accepted solutions is increased andtherefore, an increase in coverage is obtained. However, a lower-thanneeded threshold may decrease the accuracy provided by the CBE byaccepting too many incorrect solutions (False Positives). Therefore, itmay be desirable to obtain a threshold using a method that balancesthese two concepts.

[0168] According to an embodiment of the invention, coverage for anygiven threshold level r may include accepting n(r) cases out of K. Givena Case Base with K cases, the function g₁(t) may be defined as a measureor coverage:

g ₁(τ)=n(τ)/K

[0169] For accuracy, the performance function f, as previously defined,may be used (e.g., aggregate the rewards of correct classifications withthe increasing penalties for misclassifications) and may be adapted tothe entire Case Base to evaluate its accuracy for any given threshold r.As the value of r is modified, more decisions may be accepted orrejected, modifying the entries of the comparison matrix M=[M(i,j)].${g_{2}(\tau)} = {{\sum\limits_{i = 1}^{T}\quad {K*R*{M\left( {i,i} \right)}}} + {\sum\limits_{i = 1}^{T}{\sum\limits_{{j = 1},{j \neq i}}^{T}{{p\left( {i,j} \right)}*R*{M\left( {i,j} \right)}}}}}$

[0170] Specifically, the function g₂(τ) may be defined as a measure ofrelative accuracy, where M(i,j) is the (i,j) element of the comparisonmatrix illustrated in FIG. 14. It may represent the percentage of casesclassified in cell i while the correct classification was cell j.Therefore (i=j) implies a correct classification. The percentage may becomputed over the total cases for which the decision has been accepted(i.e., its corresponding confidence was above the threshold). Further,K*R may be a reward for correct classification (where K indicates astatic multiple of basic reward R), and p(i,j)*R may be the penalty forincorrect classification (p(i,j) determine a dynamic multiple of basicreward R).

[0171] For simplicity, R=1 may be used. The penalty function p(i,j) mayindicate the increasing penalty for misclassifications farther away fromthe correct one. Many possible versions of function p(i,j) can be used.By way of example, the vector W=[−11, −6, −1, 4, −1, −6, −11]corresponds to the values:

K=4 and

p(i,j)=5|i−j|+4

[0172] A linear penalty function p(i,j) is illustrated in FIG. 30. Itwill be recognized by those of ordinary skill in the art that otherlinear functions may also be used. If over-penalization for largermisclassifications is desired, a non-linear penalty function may beused, such as p(i,j)=−3(i−j)²+4,, such as that illustrated in FIG. 31.

[0173] The selection of a penalty function may be left as a choice to auser to represent the cost of different misclassifications. According toan embodiment of the invention, if there were no differences among suchcosts, then a simplified version of g₂(r) could be used to measure theCBE accuracy, e.g.:${g_{2}(\tau)} = {\sum\limits_{i = 1}^{T}\quad {K*R*{M\left( {i,i} \right)}}}$

[0174] Functions g₁(t) and g₂(t) may be defined to measure coverage andrelative accuracy, respectively. Function g₁(t) may be a monotonicallynon-increasing with the value t (larger values of t will not increasecoverage), while g₂(t) may be a monotonically non-decreasing with thevalue t (larger values of t will not decrease relative accuracy, unlessthe set is empty.). The two functions may be aggregated into a globalaccuracy function A(t) to evaluate the overall system performance underdifferent thresholds t:

A(τ)=g ₁(τ)×g ₂(τ)

[0175] where X indicates scalar multiplication

[0176] The function A(t) provides a measure of accuracy combined withthe coverage of cases. FIG. 21 illustrates an example of the computationof Coverage, Relative Accuracy, and Global Accuracy as a function ofthreshold t. In this example, t=0.1 has the largest coverage, t=0.7 hasthe largest relative accuracy, and t=0.5 has the largest globalaccuracy.

[0177] There are many approaches that may be used to maximize theaggregate function A(t) to obtain the best value for threshold t. Anyreasonable optimization algorithm (such as a gradient-based search, or acombined gradient and binary search) may be used to this effect. Forexample, in FIG. 21, the value of A(t) may be computed for nine valuesof t. According to an embodiment of the invention, values may beexplored to determine a best threshold, By way of example only, theneighborhood of t=0.5 may be explored, such as by a gradient method, todetermine that the value t=0.55 is the best threshold.

[0178] As described above, the present invention provides manyadvantages. According to an embodiment of the present invention,incremental deployment of the CBE may be achieved, instead of postponingits deployment until an entire Case Base has been completely populated.Further, a determination may be made for which applications (e.g.,characterized by specific medical conditions) the CBE can providesufficiently high confidence in the output to shift its use from a humanunderwriter productivity tool to an automated placement tool.

[0179] According to an embodiment of the invention, as the Case Base isaugmented and or updated by new resolved applications, the quality ofthe retrieved cases may change. The present invention may enablemonitoring of the quality of the Case Base, indicating the part of theCB requiring growth or scrubbing. By way of example, regions within theCase Base with insufficient coverage (small area histograms, lowsimilarity levels) may be identified, as well as regions containinginconsistent decisions (bimodal histograms), and ambiguous regions (verybroad histograms).

[0180] According to an embodiment of the invention, by establishing aconfidence threshold, a determination can be made, for each applicationprocessed by the CBE, if the output can be used directly to place theapplication or if it will be a suggestion to be revised by a humanunderwriter.

[0181] According to an embodiment of the invention, a process asdescribed above may be used after the deployment of the CBE, as part ofthe Case Base maintenance. As the Case Based is enriched by the influxof new cases, the distribution of its cases may also vary. Regions ofthe CB that were sparsely populated might now contain a larger number ofcases. Therefore, as part of the tuning of the CBE, one shouldperiodically recompute various steps within the process to update thesoft constraints on each of the parameters. As part of the samemaintenance, the value of the best threshold may also be updated andused in the process.

[0182] Network-Based Underwriting System

[0183]FIG. 22 illustrates a system 2200 according to an embodiment ofthe present invention. The system 2200 comprises a plurality of computerdevices 2205 (or “computers”) used by a plurality of users to connect toa network 2202 through a plurality of connection providers (CPs) 2210.The network 2202 may be any network that permits multiple computers toconnect and interact. According to an embodiment of the invention, thenetwork 2202 may be comprised of a dedicated line to connect theplurality of the users, such as the Internet, an intranet, a local areanetwork (LAN), a wide area network (WAN), a wireless network, or othertype of network. Each of the CPs 2210 may be a provider that connectsthe users to the network 2202. For example, the CP 2210 may be anInternet service provider (ISP), a dial-up access means, such as amodem, or other manner of connecting to the network 2202. In actualpractice, there may be significantly more users connected to the system2200 than shown in FIG. 22. This would mean that there would beadditional users who are connected through the same CPs 2210 shown orthrough another CP 2210. Nevertheless, for purposes of illustration, thediscussion will presume three computer devices 2205 are connected to thenetwork 2202 through two CPs 2210.

[0184] According to an embodiment of the invention, the computer devices2205 a-2205 c may each make use of any device (e.g., a computer, awireless telephone, a personal digital assistant, etc.) capable ofaccessing the network 2202 through the CP 2210. Alternatively, some orall of the computer devices 2205 a-2205 c may access the network 2202through a direct connection, such as a T1 line, or similar connection.FIG. 22 shows the three computer devices 2205 a-2205 c, each having aconnection to the network 2202 through the CP 2210 a and the CP 2210 b.The computer devices 2205 a-2205 c may each make use of a personalcomputer such as a computer located in a user's home, or may use otherdevices which allow the user to access and interact with others on thenetwork 2202. A central controller module 2212 may also have aconnection to the network 2202 as described above. The centralcontroller module 2212 may communicate with one or more modules, such asone or more data storage modules 2236, one or more evaluation modules2224, one or more case database modules 2240 or other modules discussedin greater detail below.

[0185] Each of the computer devices 2205 a-2205 c used may contain aprocessor module 2204, a display module 2208, and a user interfacemodule 2206. Each of the computer devices 2205 a-2205 c may have atleast one user interface module 2206 for interacting and controlling thecomputer. The user interface module 2206 may be comprised of one or moreof a keyboard, a joystick, a touchpad, a mouse, a scanner or any similardevice or combination of devices. Each of the computers 2205 a-2205 cmay also include a display module 2208, such as a CRT display or otherdevice. According to an embodiment of the invention, a developer, a userof a production system, and/or a change management module may use acomputer device 2205.

[0186] The central controller module 2212 may maintain a connection tothe network 2202 such as through a transmitter module 2214 and areceiver module 2216. The transmitter module 2214 and the receivermodule 2216 may be comprised of conventional devices that enable thecentral controller module 2212 to interact with the network 2202.According to an embodiment of the invention, the transmitter module 2214and the receiver module 2216 may be integral with the central controllermodule 2212. According to another embodiment of the invention, thetransmitter module 2214 and the receiver module 2216 may be portions ofone connection device. The connection to the network 2202 by the centralcontroller module 2212 and the computer devices 2205 may be a highspeed, large bandwidth connection, such as through a T1 or a T3 line, acable connection, a telephone line connection, a DSL connection, oranother similar type of connection. The central controller module 2212functions to permit the computer devices 2205 a-2205 c to interact witheach other in connection with various applications, messaging servicesand other services which may be provided through the system 2200.

[0187] The central controller module 2212 preferably comprises either asingle server computer or a plurality of server computers configured toappear to the computer devices 2205 a-2205 c as a single resource. Thecentral controller module 2212 communicates with a number of modules.Each module will now be described in greater detail.

[0188] A processor module 2218 may be responsible for carrying outprocessing within the system 2200. According to an embodiment of theinvention, the processor module 2218 may handle high-level processing,and may comprise a math co-processor or other processing devices.

[0189] A decision component category module 2220 and an applicationcategory module 2222 may handle categories for various insurancepolicies and decision components. As described above, each decisioncomponent and each application may be assigned a category. The decisioncomponent category module 2220 may include information related to thecategory assigned for each decision component, including across-reference to the application associated with each decisioncomponent, the assigned category or categories, and/or otherinformation. The application category module 2222 may includeinformation related to the category assigned for each application,including a cross-reference to the decision components associated witheach application, the assigned category or categories, and/or otherinformation.

[0190] An evaluation module 2224 may include an evaluation of a decisioncomponent using one or more rules, where the rules may be fuzzy logicrules. The evaluation module 2224 may direct the application of one ormore fuzzy logic rules to one or more decision components. Further, theevaluation module 2224 may direct the application of one or more fuzzylogic rules to one or more policies within a case database 2240, to bedescribed in greater detail below. Evaluation module policies within acase database 2240, are to be described in greater detail below.

[0191] A measurement module 2226 may include measurements assigned toone or more decision components. As described above, a measurement maybe assigned to each decision component based on an evaluation, such asan evaluation with a fuzzy logic rule. The measurement module 2226 mayassociate a measurement with each decision component, direct thegeneration of the measurement, and/or include information related to ameasurement.

[0192] An issue module 2228 may handle issuing an insurance policy basedon the evaluation and measurements of one or more decision componentsand the application itself. According to an embodiment of the invention,decisions whether to ultimately issue an insurance policy or not toissue an insurance policy may be communicated to an applicant throughthe issue module 2228. The issue module 2228 may associate issuance ofan insurance policy with an applicant, with various measurement(s) andevaluation(s) of one or more policies and/or decision components andother information.

[0193] A retrieval module 2230 may be responsible for retrieving casesfrom a case database module 2240. According to an embodiment of theinvention, queries submitted by a user for case-based reasoning may becoordinated through the retrieval module 2230 for retrieving cases.Other information and functions related for case retrieval may also beavailable.

[0194] A ranking module 2232 may be responsible for ranking casesretrieved based on one or more queries received from a user. Accordingto an embodiment of the invention, the ranking module 2232 may maintaininformation related to cases and associated with one or more queries.The ranking module 2232 may associate each case with the ranking(s)associated with one or more queries. Other information may also beassociated with the ranking module 2232.

[0195] A rate class module 2234 may handle various designations of rateclasses for one or more insurance policies. According to an embodimentof the invention, each application may be assigned a rate class, wherethe premiums paid by the applicant are based on the rate class. The rateclass module 2234 may associate a rate class with each insuranceapplication, and may assign a rate class based on evaluation andmeasurements of various applications and decision components, as well asbased on a decision by one or more underwriters. Other information mayalso be associated with the rate class module 2234.

[0196] Data may be stored in a data storage module 2236. The datastorage module 2236 stores a plurality of digital files. According to anembodiment of the invention, a plurality of data storage modules 2236may be used and located on one or more data storage devices, where thedata storage devices are combined or separate from the controller module2212. One or more data storage modules 2236 may also be used to archiveinformation.

[0197] An adaptation module 2238 may be responsible for adapting theresults of one or more queries to determine which previous cases aremost similar to the application for the present application forinsurance. Other information may also be associated with the adaptationmodule 2238.

[0198] All cases used in a case based reasoning may be stored in a casedatabase module 2240. According to an embodiment of the invention, aplurality of case database modules 2240 may be used and located on oneor more data storage devices, where the data storage devices arecombined or separate from the controller module 2212.

[0199] While the system 2200 of FIG. 22 discloses the requester device2205 connected to the network 2202, it should be understood that apersonal digital assistant (“PDA”), a mobile telephone, a television, oranother device that permits access to the network 2202 may be used toarrive at the system of the present invention.

[0200] According to another embodiment of the invention, acomputer-usable and writeable medium having a plurality of computerreadable program code stored therein may be provided for practicing theprocess of the present invention. The process and system of the presentinvention may be implemented within a variety of operating systems, suchas a Windows® operating system, various versions of a Unix-basedoperating system (e.g., a Hewlett Packard, a Red Hat, or a Linux versionof a Unix-based operating system), or various versions of anAS/400-based operating system. For example, the computer-usable andwriteable medium may be comprised of a CD ROM, a floppy disk, a harddisk, or any other computer-usable medium. One or more of the componentsof the system 2200 may comprise computer readable program code in theform of functional instructions stored in the computer-usable mediumsuch that when the computer-usable medium is installed on the system2200, those components cause the system 2200 to perform the functionsdescribed. The computer readable program code for the present inventionmay also be bundled with other computer readable program software.

[0201] According to one embodiment, the central controller module 2212,the transmitter module 2214, the receiver module 2216, the processormodule 2218, the decision component category module 2220, applicationcategory module 2222, evaluation module 2224, measurement module 2226,issue module 2228, retrieval module 2230, ranking module 2232, rateclass module 2234, data storage module 2236, adaptation module 2238, andcase database module 2240 may each comprise computer-readable code that,when installed on a computer, performs the functions described above.Also, only some of the components may be provided in computer-readablecode.

[0202] Additionally, various entities and combinations of entities mayemploy a computer to implement the components performing theabove-described functions. According to an embodiment of the invention,the computer may be a standard computer comprising an input device, anoutput device, a processor device, and a data storage device. Accordingto other embodiments of the invention, various components may becomputers in different departments within the same corporation orentity. Other computer configurations may also be used. According toanother embodiment of the invention, various components may be separateentities such as corporations or limited liability companies. Otherembodiments, in compliance with applicable laws and regulations, mayalso be used.

[0203] According to one specific embodiment of the present invention,the system may comprise components of a software system. The system mayoperate on a network and may be connected to other systems sharing acommon database. Other hardware arrangements may also be provided.

[0204] Other embodiments, uses and advantages of the present inventionwill be apparent to those skilled in the art from consideration of thespecification and practice of the invention disclosed herein. Thespecification and examples should be considered exemplary only. Theintended scope of the invention is only limited by the claims appendedhereto.

[0205] Information Summarization

[0206] The fuzzy rule-based decision engine and the case-based decisionengine may need to capture the medical/actuarial knowledge required toevaluate and underwrite an application. They may do so by using a ruleset or a case base, respectively. However, both decision engines mayalso need access to all the relevant information that characterizes thenew application. While the structured component of this information canbe captured as data and stored into a database (“DB”), the free-formnature of an attending physician statement (APS) may not be suitable toautomated parsing and interpretation. Therefore, for each applicationrequiring an APS, a summarization tool may be used that will convert allthe essential input variables from that statement into a structuredform, suitable for storage in a DB and for supporting automated decisionsystems. Furthermore, if the decision engines were not capable ofhandling this new application, then the use of the APS summarizationtool may be a productivity aid for a human underwriter, rather than anautomation tool.

[0207] The present invention may be used in connection with an engine toautomate decisions in business, commercial, or manufacturing processes.Such an engine may be based on (but not limited to) rules and/or cases.A process and system may be provided to structure and summarize keyinformation required by a reasoning system. According to an embodimentof the invention, summarized information required by a reasoning systemmay be used to underwrite insurance applications, and establish a rateclass corresponding to the perceived risk of the applicant. Such riskmay be characterized by several information sources, such as, but notlimited to, the application form, the APS, laboratory data, medicalinsurance consortium data bases, motor vehicle registration data bases,etc. Once this information has been gathered and compiled, theapplication risk may be evaluated by a human underwriter or by anautomated decision system. This evaluation is carried out leveraging themedical and actuarial knowledge of the human underwriter, which iscaptured in its essence by the automated reasoning system.

[0208] According to an embodiment of the invention, an APS summarizationtool may capture the relevant variables that characterize a givenmedical impairment, allowing an automated reasoning system to determinethe degree of severity of such impairment and to estimate the underlyinginsurance risk.

[0209] According to an embodiment of the invention, a focus of thisinvention on the individual medical impairments of a patient mayprovide 1) incremental deployment of the Automated Underwriting systemas summaries for new impairments can be developed and added; 2)efficient coverage, by addressing the most frequent impairments first,according to a Pareto analysis of their frequencies; 3) efficientdescription of the impairment, by including in the summary only thevariables that could have an impact on the decision.

[0210] By way of example, an aspect of the present invention will bedescribed in terms of underwriting of an application for a fixed lifeinsurance policy. Although the description focuses on the use of areasoning system to automate the underwriting process of insurancepolicies, it will be understood by one of ordinary skill in the art thatthe applicability of this invention may be much broader, as it may applyto other reasoning system applications.

[0211] According to an embodiment of the invention, a method forexecuting and manipulating an APS summarization tool may occur asillustrated in FIG. 23. At step 2300, a summarizer with the appropriatemedical knowledge would log into a web-based system to begin thesummarization process. According to an embodiment of the invention, theAPS summarization system may include a general form plus variouscondition specific forms, which are then filled out by the summarizer.The summarizer may first fill out the general form, which contains datafields relevant to all applicants. Condition specific forms are thenfilled out as needed, as the summarizer discovers various featurespresent in the APS being summarized.

[0212] At step 2302, a summarizer may verify that the APS corresponds tothe correct applicant. This may be done by matching information on theAPS itself with information about the applicant provided by the system.By way of example, an applicant's name, date of birth, and socialsecurity number could be matched. If a match is not made, the summarizermay note this by checking the appropriate checkbox. According to anembodiment of the invention, at step 2304, failure to match an APS to anapplicant would end the summarizer's session for that applicant, and thesummarizer may recommend corrective action.

[0213] At step 2306, the general form is filled out. FIG. 24 illustratesa general form within a graphical user interface 2400 according to anembodiment of the invention. Graphical user interface 2400 may compriseaccess to any network browser, such as Netscape Navigator, MicrosoftExplorer, or others. Other means of accessing a network may also beused. Graphical user interface 2400 may include a control area 2402,whereby a summarizer may control various aspects of graphical userinterface 2400. Control may include moving to various portions of thenetwork via the graphical user interface 2400, printing information fromthe network, searching for information within the network, and otherfunctions used within a browser.

[0214] According to an embodiment of the invention, a general form 2400may provide a fixed structure 2406 to capture the data within thesystem. According to an embodiment of the invention, different sectionsof the form may be organized into fields that are structured to provideonly a fixed set of choices for the summarizer. This may be done tostandardize the different pieces of information contained in the APS. Byway of example, a fixed set of choices may be provided to a summarizervia a pull-down menu 2408. For fields that cannot be treated aspull-down menus (e.g., dates, numeric values of lab tests), such asentry field 2410 labeled as “Initial date,” validation may be performedto ensure that data entry errors are minimized, and to check that valuesare within allowable pre-determined limits. According to an embodimentof the invention, validation may include a “client-side” validation,designed to give the summarizer an immediate response if any of the datais incorrectly entered. A “client-side” validation may be achievedthrough JavaScript code embedded in the web pages. According to anembodiment of the invention, validation may include a “server-side”validation, which may be performed after data submission. “Server-side”validation may be designed primarily as a fail-safe check to preventerroneous data from entering the business-critical database.

[0215] According to an embodiment of the invention, link section 2404may provide access to other portions of general form 2400. Asillustrated in FIG. 24, link section 2404 may include links (such ashypertext links) to portions of general form 2400 that relate to bloodpressure, family history, nicotine use, build, lipids, alcohol use,cardiovascular fitness and tests, final check, comments, abnormalphysical symptoms, abnormal blood results, abnormal urine results,abnormal pap test, mammogram, abnormal colonoscopy, chest x-ray,pulmonary function, substance abuse, and non-medical history. Otherinformation within a general form 2400 may also be provided, and assuch, may be linked through link section 2404.

[0216] According to an embodiment of the invention, an APS summary maydistinguish between a blank data field and answers such as “don't know”or “not applicable,” thereby ensuring the completeness of the summary.For a general form submission, a final validation pass may be performedat step 2308 to alert the summarizer if certain required fields areblank. If required fields are blank, the system may require a summarizerto return to step 2306 and complete the general form. If the summarizerwishes to indicate that the particular piece of information is notknown, they may be required to specifically indicate so, therebymaintaining information about what information is specifically notknown. However, it will be recognized that not all fields willnecessarily require information. For example, certain fields may be“conditionally mandatory,” meaning that they require an answer only ifother fields have been filled out in a particular way. Use ofconditionally mandatory fields may ensure that all necessary informationis gathered. Further, ensuring that all required fields have been filledmay also ensure that the necessary information is gathered.

[0217] When the general form has been filled out and validated at step2308, with all of the required fields entered, it may be necessary tocomplete one or more condition-specific forms. At step 2310, it isdetermined if any condition-specific forms are required. If no conditionspecific forms are required, the results may be submitted to a databaseor other storage device for use at a later time at step 2320.

[0218] If a condition-specific form is required, a summarizer may selecta condition-specific form to fill-in at step 2312. According to anembodiment of the invention, a summarizer may move from the general formto any of the condition-specific forms by following a hypertext linkembedded within the general form. By way of example, a link to acondition-specific form may be similar to, and/or same as links locatedwithin link portion 2404. Further, links to condition-specific forms maybe located within link portion 2404. A portion of the knowledge of whichcondition-specific forms are necessary may be obtained while filling outthe general form. In the current example of life insurance underwriting,these condition-specific forms may include hypertension, diabetes, etc.

[0219]FIG. 25 illustrates an example of a condition-specific form forhypertension within a graphical user interface 2500 according to anembodiment of the invention. Graphical user interface 2500 may compriseaccess to any network browser, such as Netscape Navigator, MicrosoftExplorer, or other browser. Other manners of accessing a network mayalso be used. Graphical user interface 2500 may include a control area2502, whereby a summarizer may control various aspects of graphic userinterface 2500. Control may include moving to various portions of thenetwork via the graphic user interface 2500, printing information fromthe network, searching for information within the network, and otherfunctions used within a browser.

[0220] Graphical user interface 2500 displays the hypertension-specificform, which may include various sections for inputting informationrelated to hypertension. In the hypertension specific form illustratedin FIG. 25, initial identification section 2504 may enable a summarizerto provide initial identification information, including whether anapplicant has hypertension, the type of hypertension, whether it wassecondary hypertension, and if so, how the cause was removed or cured.According to an embodiment of the invention, pull down menus may be usedto ensure that information entered is standardized for each patient.Other information may also be gathered in initial identification section2504.

[0221] EKG section 2506 may enable a summarizer to provide EKGinformation, including EKG readings within a specified time period(e.g., 6 months), chest X-rays within a specified time period (e.g., 6months), and other information related to EKG readings. According to anembodiment of the invention, pull down menus may be used to ensure thatinformation entered is standardized for each patient. Patientcooperation section 2508 may enable a summarizer to provide informationrelated to a patient's cooperation, including whether the patient hascooperated, whether a patient's blood pressure is under control, and ifso, for how many months, and other information related to a patient'scooperation in dealing with hypertension. According to an embodiment ofthe invention, pull down menus may be used to ensure that informationentered is standardized for each patient.

[0222] Blood pressure section 2510 may enable a summarizer to enterblood pressure readings corresponding to various dates. According to anembodiment of the invention, separate entry fields may be provided forthe date the blood pressure reading was taken, (e.g., systolic reading(SBP) and the diastolic reading (DBP)). Other information may also beentered in blood pressure section 2510. Further, it will be understoodby those skilled in the art that other information related tohypertension may also be entered in a hypertension form displayed ongraphical user interface 2500.

[0223] At step 2314, a summarizer fills out a condition-specific form.For a condition-specific form, a final validation pass may be performedat step 2316 to alert the summarizer if certain required fields areblank. If required fields are blank, the system may require a summarizerto return to step 2314 and complete the condition-specific form. As witha general form, if the summarizer wishes to indicate that the particularpiece of information is not known, they may be required to specificallyindicate so, thereby facilitating the tracking of what information isspecifically not known. However, it will be recognized that not allfields will necessarily require information. For example, certain fieldsmay be “conditionally mandatory,” meaning that they require an answeronly if other fields have been filled out in a particular way. Use ofconditionally mandatory fields may ensure that all necessary informationis gathered. Further, ensuring that all required fields have been filledmay also ensure that the necessary information is gathered.

[0224] If the condition-specific form has been filled out and validatedat step 2316, with all of the required fields entered, a summarizer maydetermine if additional condition-specific forms are necessary at step2318. If additional condition-specific forms are necessary, a summarizermay return to step 2312 and select the appropriate condition-specificform in which to enter information. If no additional condition-specificforms are required, the results may be submitted to a database or otherstorage device for use at a later time at step 2320.

[0225] Once the summarization is complete for a general form and anyselected condition-specific forms, the summarizer may submit theresults, such as described in step 2320. The data may then betransferred over a network, such as the Internet, and stored in adatabase for later use. According to an embodiment of the invention,different categorical data fields may be presented to the summarizer astext, but for space efficiency are encoded as integer values in thedatabase. A “translation table” to the corresponding field meanings maythen be provided as part of the design of the APS summary. The APSsummarizer may provide a structured list of topics, thereby enabling atrained person to summarize the most significant information currentlycontained in a handwritten or typewritten APS. Further, the APSsummarizer may provide an efficient description of the data content ofthe APS. As stated above, the APS itself can be several tens of pages ofdoctor's notes. The APS summary is designed to capture only the datafields that are relevant to the problem at hand. In addition, astructured and organized description of the APS data may be provided. AnAPS itself can adhere to any arbitrary order because of differentdoctor's styles. The APS summary may provide a single consistent formatfor the data as required for an automated system, and/or whichfacilitates the human underwriter's job greatly.

[0226] Since the APS summary may be captured in a database, theinformation contained in it may be easily available to anycomputer-based application. Again, this is a requirement for anautomated underwriting system, but it may provide many other advantagesas well. For example, the APS data may otherwise be very difficult toanalyze statistically, to categorize, or to classify. Since the APSsummary forms can be web-based, the physical location of the summarizersmay be immaterial. The original APS sheets can be received in locationX, scanned, sent over the Internet to location Y, where the APS summaryis filled out, and the digital data from the summary can be submittedand stored on a database server in location Z. Further, the automateddecision engine can be in any fourth location, as could an individualrunning queries against the APS summary database for statisticalanalysis or reporting purposes.

[0227] According to an embodiment of the invention, general andcondition specific forms may be written in HTML and JavaScript, whichprovide the validation functionality. A system for storing filled outsummary data into a remote database has also been created. This systemwas created using JavaBeans and JSP. Testing by experienced underwritershas been performed. The HTML summary forms are displayed to theunderwriters via a web browser, and the data from an actual APS isentered onto the form. The underwriter comments and feedback arecaptured on the form as well, and used to aid the continual improvementof the forms. In choosing which condition-specific forms to create, astatistical analysis was done of the frequencies of the various medicalconditions. The conditions that are most frequent were chosen to beworked on first. The APS summary does not have to cover all conditionsbefore it is put into production. Deployment of the APS summary may beprogressive, covering new conditions one by one as new forms becomeavailable. Applicants with APS requirements that are not covered in thecurrent APS summary may be underwritten using the usual procedures.Condition-specific forms may therefore be added to the APS summary inorder to increase coverage of applicants by the digital underwritingsystem.

[0228] Optimization of Fuzzy Rule-Based and Case-Based Decision Engines

[0229] According to an embodiment of the present invention, fuzzyrule-based and case-based reasoning may be used to automate decisions inbusiness, commercial, or manufacturing process. Specifically, a processand system to automate the determination of optimal design parametersthat impact the quality of the output of the decision engines isdescribed.

[0230] According to an embodiment of the invention, the optimizationaspect may provide a structured and robust search and optimizationmethodology for identifying and tuning the decision thresholds (cutoffs)of the fuzzy rules and internal parameters of the fuzzy rule-baseddecision engine (“RBE”), and the internal parameters of the case-baseddecision engine (“CBE”). These benefits may include a minimization ofthe degree of rate class assignment mismatch between that of an experthuman underwriter and automated rate class decisions. Further, themaintenance of the accuracy of rule-based and case-based decision-makingas decision guidelines evolve with time may be achieved. In addition,identification of ideal parameter combinations that govern the automateddecision-making process may occur.

[0231] The system and process of the present invention may apply to aclass of stochastic global search algorithms known as evolutionaryalgorithms to perform parameter identification and tuning. Suchalgorithms may be executed utilizing principles of natural evolution andmay be robust adaptive search schemes suitable for searching non-linear,discontinuous, and high-dimensional spaces. Moreover, this tuningapproach may not require an explicit mathematical description of themulti-dimensional search space. Instead, this tuning approach may relysolely on an objective function that is capable of producing a relativemeasure of alternative solutions. According to an embodiment of theinvention, an evolutionary algorithm may be used for optimization withinan RBE and CBE. By way of example, an evolutionary algorithm (“EA”) mayinclude genetic algorithms, evolutionary programming, evolutionstrategies, and genetic programming. The principles of these relatedtechniques may define a general paradigm that is based on a simulationof natural evolution. EAs may perform their search by maintaining at anytime t a population P(t)={P₁(t), P₂(t), . . . , P_(p)(t)} ofindividuals. In this example, “genetic” operators that model simplifiedrules of biological evolution are applied to create the new anddesirably more superior population P(t+1). Such a process may continueuntil a sufficiently good population is achieved, or some othertermination condition is satisfied. Each P_(i)(t)εP(t), represents viaan internal data structure, a potential solution to the originalproblem. The choice of an appropriate data structure for representingsolutions may be more an “art” than a “science” due to the plurality ofdata structures suitable for a given problem. However, the choice of anappropriate representation may be a critical step in a successfulapplication of EAs. Effort may be required to select a data structurethat is compact, minimally superfluous, and can avoid creation ofinfeasible individuals. For instance, if the problem domain requiresfinding an optimal real vector from the space defined by dissimilarlybounded real coordinates, it may be more appropriate to choose as arepresentation a real-set-array (e.g., bounded sets of real numbers)instead of a representation capable of generating bit strings. Arepresentation that generates bit strings may create many infeasibleindividuals, and can be certainly longer than a more compact sequence ofreal numbers. Closely linked to a choice of representation of solutionsmay be a choice of a fitness function ψ: P(t)→R, that assigns credit tocandidate solutions. Individuals in a population are assigned fitnessvalues according to some evaluation criterion. Fitness values maymeasure how well individuals represent solutions to the problem. Highlyfit individuals are more likely to create offspring by recombination ormutation operations. Weak individuals are less likely to be picked forreproduction, so they eventually die out. A mutation operator introducesgenetic variations in the population by randomly modifying some of thebuilding blocks of individuals. Evolutionary algorithms are essentiallyparallel by design, and at each evolutionary step a breadth search ofincreasingly optimal sub-regions of the options space is performed.Evolutionary search is a powerful technique of solving problems, and isapplicable to a wide variety of practical problems that are nearlyintractable with other conventional optimization techniques. Practicalevolutionary search schemes do not guarantee convergence to the globaloptimum in a predetermined finite time, but they are often capable offinding very good and consistent approximate solutions. However, theyare shown to asymptotically converge under mild conditions.

[0232] An evolutionary algorithm may be used within a process and systemfor automating the tuning and maintenance of fuzzy rule-based andcase-based decision systems used for automated decisions in insuranceunderwriting. While this approach is demonstrated for insuranceunderwriting, it is broadly applicable to diverse rule-based andcase-based decision-making applications in business, commercial, andmanufacturing processes. Specifically, we describe a structured androbust search and optimization methodology based on a configurablemulti-stage evolutionary algorithm for identifying and tuning thedecision thresholds of the fuzzy rules and internal parameters of thefuzzy rule-based decision engine and the internal parameters of thecase-based decision engine. The parameters of the decision systemsimpact the quality of the decision-making, and are therefore critical.Furthermore, this tuning methodology can be used periodically to updateand maintain the decision engines.

[0233] As stated above, these fuzzy logic systems may have manyparameters that can be freely chosen. These parameters may either be fitto reproduce a given set of decisions, or set by management in order toachieve certain results, or a combination of the two. A large set ofcases may be provided by the company as a “certified case base.”According to an embodiment of the invention, the statistics of thecertified case base may closely match the statistics of insuranceapplications received in a reasonable time window. According to anembodiment of the invention, there will be many more cases than freeparameters, so that the system will be over-determined. Then, an optimalsolution may be found which minimizes the classification error between adecision engine's output and the supplied cases. When consideringmaintenance of a system, it may be convenient and advantageous that theparameters are chosen using optimization vs. a set of certified cases.New fuzzy rules and certified cases may be added, or aggregation rulesmay change. The fuzzy logic systems may be kept current, allowing theinsurance company to implement changes quickly and with zerovariability.

[0234] The parameter identification and tuning problem which maypresented in this invention can be mathematically described as aminimization problem:

min ψ(x)where χ=χ₁×χ₂× . . . ×χ_(n) χ_(i)⊂

and ψ:χ→

₊ ^(xε)χ

[0235] where χ is an n-dimensional bounded hyper-volume (parametricsearch space) in the n-dimensional space of reals, x is a parametervector, and ψ is the objective function that maps the parametric searchspace to the non-negative real line.

[0236]FIG. 26 illustrates such a minimization (optimization) problemaccording to an embodiment of the invention in the context of theapplication domain, where the search space χ corresponds to the space ofdecision engine designs induced by the parameters imbedded in thedecision engine, and the objective function ψ measures the correspondingdegree of rate-class assignment mismatch between that of the experthuman underwriter and the decision-engine for the certified case base.An evolutionary algorithm iteratively generates trial solutions (trialparameter vectors in the space χ), and uses their correspondingconsequent degree of rate-class assignment mismatch as the searchfeedback. Thus, at step 2602, a space of decision engine's designs isprobed. At step 2604, a mismatch matrix, which will be described ingreater detail below, is generated based on the rate-class decisionsgenerated for the cases by the decision engine. Penalties formismatching cases are assigned at step 2606. The evolutionary algorithmuses the corresponding degree of rate-class assignment mismatches, andthe associated penalties to provide feedback to the decision engine atstep 2608. The system may then refine the internal parameters anddecision thresholds in the decision engine at step 2602, and proceedthrough the process again. Thus, an iterative process may be performed.

[0237]FIG. 27 illustrates an example of an encoded population maintainedby the evolutionary algorithm at a given generation. According to anembodiment of the invention, each individual in the population is atrial vector of design parameters representing fuzzy rule thresholds andinternal parameters of the decision engine. Each percentage entry mayrepresent a value of a trial parameter that falls within a correspondingbounded real line. Each trial solution vector may be used to initializean instance of the decision engine, following which each of the cases inthe certified case base is evaluated.

[0238]FIG. 28 illustrates a process schematic for an evaluation systemaccording to an embodiment of the invention. Trial design parameters areprovided at an input module 2802. The trial design parameters areautomatically input to decision engine 2804. Case subset 2808 fromcertified case base 2806 is input into decision engine 2804. Certifiedcase base 2806 may comprises cases that have been certified as beingcorrect. Case subset 2808 may be a predetermined number of cases fromcertified case base 2806. According to an embodiment of the invention,case subset 2808 may comprise two thousand (2000) certified cases.According to an embodiment of the invention, case subset 2808 maycomprise a number of times the number of tunable parameters of decisionengine 2804. The cases within case subset 2808 are processed in decisionengine 2804, and output to decision engine case decisions 2810.

[0239] Once all the cases in the certified case base are evaluated, asquare confusion matrix 2814 is created. According to an embodiment ofthe invention, confusion matrix 2814 may be generated by comparingdecision engine case decisions 2810 and certified case decisions 2812.The rows of confusion matrix 2814 may correspond to certified casedecisions 2812 as determined by an expert human underwriter, and thecolumns of confusion matrix 2814 may correspond to the decision enginecase decisions 2810 for the cases in the certified case base. By way ofexample, assume a case has been assigned a category S from certifiedcase decision 2812 (from the matrix 2814) and a category PB fromdecision engine decision 2810. Under these categorizations, the casewould count towards an entry in the cell at row 3 and column 1. In thisexample, the certified case decision 2812 places the case in a higherrisk category, while the decision engine case decision 2810 places thecase in a lower risk category. Therefore, for this particular case, thedecision engine 2810 has been more liberal in decision-making. By way ofanother example, if on the other hand both the certified case decision2812 and the decision engine case decisions 2810 agree as uponcategorizing the case in class S, then the case would count towards anentry in the cell at row 3 and column 3. By way of another example, ifthe certified case decision 2812 is PB, but the machine decision 2810 isS, then clearly the machine decision is more strict.

[0240] According to an embodiment of the invention, it may be desirableto use a decision engine that is able to place the maximum number ofcertified cases along the main diagonal of confusion matrix 2814. It mayalso be desirable to determine those parameters 2802 for decision engine2804 that produce such results (e.g., minimize the degree of rate classassignment confusion or mismatch between certified case decisions 2812and decision engine case decisions 2810). Confusion matrix 2814 may beused as the foundation to compute an aggregate mismatch penalty orscore, using penalty module 2816. According to an embodiment of theinvention, a penalty matrix may be derived from actuarial studies and iselement-by-element multiplied with the cells of the confusion matrix2814 to generate an aggregate penalty/score for a trial vector ofparameters in the evolutionary search. A summation over the number ofrows and columns of the matrix may occur, and that should now be “T”(upper case T), as the confusion matrix M may be of a dimension T×T.Other process systems may also be used to achieve the present invention.

[0241] According to an embodiment of the invention, an evolutionaryalgorithm may utilize only the selection and stochastic variation(mutation) operations to evolve generations of trial solutions. Whilethe selection operation may seek to exploit known search space regions,the mutation operation may seek to explore new regions of the searchspace. Such an algorithm is known to those of ordinary skill in the art.One example of the theoretical foundation for such an algorithm classappears in Modeling and Convergence Analysis of DistributedCoevolutionary Algorithms, Raj Subbu and Arthur C. Sanderson,Proceedings of the IEEE International Congress on EvolutionaryComputation, 2000.

[0242]FIG. 29 illustrates an example of the mechanics of such anevolutionary process. At step 2902, an initial population of trialdecision engine parameters is created. Proportional selection occurs atstep 2904 and an intermediate population is created at step 2906.Stochastic variation occurs at step 2908, and a new population iscreated at step 2910. The new population may then be subject toproportional selection at step 2904, thereby creating an iterativeprocess.

[0243] According to an embodiment of the invention, the evolutionaryalgorithm may use a specified fixed population size and operate in oneor more stages, each stage of which may be user configurable. A stage isspecified by a tuple consisting of a fixed number of generations andnormalized spread of a Gaussian distribution governing randomizedsampling. A given solution (also called the parent) in generation i maybe improved by cloning it to create two identical child solutions fromthe parent solution.

[0244] The first child solution may be mutated according to a uniformdistribution within the allowable search bounds. The second childsolution may then be mutated according to the Gaussian distribution forgeneration i. If the mutated solution falls outside of the allowablesearch bounds, then the sampling is repeated a few times until anacceptable sample is found. If no acceptable sample is found within theallotted number of trials, then the second child solution may be mutatedaccording to a uniform distribution. The best of the parent and twochild solutions is retained and is transferred to the population atgeneration i+1. In addition, it is ensured via elitism that theimprovement in the best performing individual of each generation ofevolution i+n (where n is an increasing whole number) is a monotonefunction. According to an embodiment of the invention, the process maybe repeated until i+n generation has been generated, where i+n is awhole number.

[0245] While the invention has been particularly shown and describedwithin the framework of an insurance underwriting application, it willbe appreciated that variations and modifications can be effected by aperson of ordinary skill in the art without departing from the scope ofthe invention. For example, one of ordinary skill in the art willrecognize that the fuzzy rule-based or case-based engine of thisinvention can be applied to any other transaction-oriented process inwhich underlying risk estimation is required to determine the pricestructure (premium, price, commission, etc.) of an offered product, suchas insurance, re-insurance, annuities, etc. Furthermore, thedetermination of the confidence factor and the optimization of thedecision engines transcend the scope of insurance underwriting. Aconfidence factor obtained in the manner described in this documentcould be determined from any application of a case-based reasoner(whether it is fuzzy or not). Similarly, the engine optimization processdescribed in this document can be applied to any engine in which thestructure of the engine has been defined and the parametric values ofthe engine need to be specified to meet a predefined performance metric.Furthermore, one of ordinary skill in the art will recognize that suchdecision engines do not need to be restricted to insurance underwritingapplications.

In the claims:
 1. A process for underwriting a pending insuranceapplication based on a comparison with at least one of a plurality ofprevious insurance application underwriting decisions, the processcomprising: receiving a request to underwrite the pending insuranceapplication, where the request includes information about at least oneapplication component; retrieving a plurality of relevant insuranceapplications from the plurality of previous insurance applicationunderwriting decisions based on a similarity between the at least oneapplication component and at least one corresponding applicationcomponent in the retrieved relevant insurance application; ranking theplurality of retrieved insurance applications according to a level ofsimilarity between the at least one application component and at leastone corresponding application component in the retrieved insuranceapplications; selecting at least one best insurance application from theplurality of retrieved insurance applications; adapting a solution tothe pending insurance application based on the at least one bestinsurance application; and evaluating the solution using a confidencefactor, where the confidence factor is based on the reliability of thesolution.
 2. The process according claim 1, further comprising the stepof storing the solution for the pending insurance application in a casememory, wherein the case memory also stores the plurality of previousinsurance application underwriting decisions.
 3. The process accordingto claim 1, further comprising the step of transmitting the results ofthe underwriting decision.
 4. The process according to claim 1, whereinthe step of retrieving employs at least one fuzzy logic rule to retrieveplurality of previous insurance application underwriting decisions. 5.The process according to claim 4, wherein the at least one fuzzy logicrule is a membership function that interprets the degree to which avalue is around a specified parameter.
 6. The process according to claim4, wherein the at least one fuzzy logic rule is a membership functionthat interprets the degree to which a value is within a core of aspecified parameter.
 7. The process according to claim 1, wherein thestep of ranking the plurality of retrieved insurance applicationsincludes deriving a distribution of the plurality of retrieved insuranceapplications based on predetermined criteria.
 8. A process for rating anapplication, where the application includes at least one category, theprocess comprising: determining a value for the at least one category ofthe application; creating a query based on the value of the at least onecategory; retrieving at least one case; and assigning the application arate classification based on the at least one retrieved case.
 9. Theprocess according to claim 8, where a plurality of cases are retrieved,and further comprising the step of generating a distribution of theplurality of cases based on the class rate assigned each of the cases.10. The process according to claim 8, where a plurality of cases areretrieved, and further comprising the step of determining a minimumsimilarity value between the plurality of retrieved cases and theapplication.
 11. The process according to claim 8, where a plurality ofcases are retrieved, and further comprising the step of generating apseudo-histogram based on the class rate assigned each of the cases. 12.The process according to claim 8, where the retrieved cases are storedin a case database, and further comprising the step of storing theapplication in the case database once the rate classification has beenassigned.
 13. The process according to claim 8, where the application isan application for an insurance application, and where the cases areinsurance application cases.
 14. A process for rating an insuranceapplication, where the insurance application includes at least onecategory, the process comprising: determining a value for the at leastone category of the insurance application; creating a query based on thevalue of the at least one category; retrieving at least one insuranceapplication case from an insurance application case database; assigningthe insurance application a rate classification based on the at leastone retrieved insurance application case; and storing the application inthe insurance application case database once the rate classification hasbeen assigned.
 15. The process according to claim 14, where a pluralityof insurance application cases are retrieved, and further comprising thestep of generating a distribution of the plurality of insuranceapplication cases based on the class rate assigned each of the insuranceapplication cases.
 16. The process according to claim 14, where aplurality of insurance application cases are retrieved, and furthercomprising the step of determining a minimum similarity value betweenthe plurality of retrieved insurance application cases and the insuranceapplication.
 17. The process according to claim 14, where a plurality ofinsurance application cases are retrieved, and further comprising thestep of generating a pseudo-histogram based on the class rate assignedeach of the insurance application cases.
 18. A computer program storedon a computer readable medium, wherein the computer program causes acomputer to perform the steps of: receiving a request to underwrite thepending insurance application, where the request includes informationabout at least one application component; retrieving a plurality ofrelevant insurance applications from the plurality of previous insuranceapplication underwriting decisions based on a similarity between the atleast one application component and at least one correspondingapplication component in the retrieved relevant insurance application;ranking the plurality of retrieved insurance applications according to alevel of similarity between the at least one application component andat least one corresponding application component in the retrievedinsurance applications; selecting at least one best insuranceapplication from the plurality of retrieved insurance applications;adapting a solution to the pending insurance application based on the atleast one best insurance application; and evaluating the solution usinga confidence factor, where the confidence factor is based on thereliability of the solution.
 19. The computer readable medium accordingto claim 18, further comprising the step of storing the solution for thepending insurance application in a case memory, wherein the case memoryalso stores the plurality of previous insurance application underwritingdecisions.
 20. The computer readable medium according to claim 18,further comprising the step of transmitting the results of theunderwriting decision.
 21. The computer readable medium according toclaim 18, wherein the step of retrieving employs at least one fuzzylogic rule to retrieve plurality of previous insurance applicationunderwriting decisions.
 22. The computer readable medium according toclaim 21, wherein the at least one fuzzy logic rule is a membershipfunction that interprets the degree to which a value is around aspecified parameter.
 23. The computer readable medium according to claim21, wherein the at least one fuzzy logic rule is a membership functionthat interprets the degree to which a value is within a core of aspecified parameter.
 24. The computer readable medium according to claim18, wherein the step of ranking the plurality of retrieved insuranceapplications includes deriving a distribution of the plurality ofretrieved insurance applications based on predetermined criteria.
 25. Acomputer program stored on a computer readable medium, wherein thecomputer program causes a computer to perform the steps of: determininga value for the at least one category of the application; creating aquery based on the value of the at least one category; retrieving atleast one case; and assigning the application a rate classificationbased on the at least one retrieved case.
 26. The computer readablemedium according to claim 25, where a plurality of cases are retrieved,and further comprising the step of generating a distribution of theplurality of cases based on the class rate assigned each of the cases.27. The computer readable medium according to claim 25, where aplurality of cases are retrieved, and further comprising the step ofdetermining a minimum similarity value between the plurality ofretrieved cases and the application.
 28. The computer readable mediumaccording to claim 25, where a plurality of cases are retrieved, andfurther comprising the step of generating a pseudo-histogram based onthe class rate assigned each of the cases.
 29. The computer readablemedium according to claim 25, where the retrieved cases are stored in acase database, and further comprising the step of storing theapplication in the case database once the rate classification has beenassigned.
 30. The computer readable medium according to claim 25, wherethe application is an application for an insurance application, andwhere the cases are insurance application cases.
 31. A process forunderwriting a transaction oriented process based on a comparison withat least one of a plurality of previous transaction underwritingdecisions, the process comprising: receiving a request to underwrite thetransaction, where the request includes information about at least onetransaction component; retrieving a plurality of relevant transactionsfrom the plurality of previous transaction underwriting decisions basedon a similarity between the at least one transaction component and atleast one corresponding transaction component in the retrieved relevanttransaction, wherein the step of retrieving employs at least one fuzzylogic rule to retrieve plurality of previous transaction underwritingdecisions; ranking the plurality of retrieved transactions according toa level of similarity between the at least one transaction component andat least one corresponding transaction component in the retrievedtransactions; selecting at least one best transaction from the pluralityof retrieved transactions; adapting a solution to the pendingtransaction based on the at least one best insurance transaction;evaluating the solution using a confidence factor, where the confidencefactor is based on the reliability of the solution; storing the solutionfor the transactions in a case memory, wherein the case memory alsostores the plurality of previous transactions underwriting decisions;and transmitting the results of the underwriting decision.
 32. Theprocess according to claim 31, wherein the at least one fuzzy logic ruleis a membership function that interprets the degree to which a value isaround a specified parameter.
 33. The process according to claim 31,wherein the at least one fuzzy logic rule is a membership function thatinterprets the degree to which a value is within a core of a specifiedparameter.
 34. The process according to claim 31, wherein the step ofranking the plurality of retrieved transactions includes deriving adistribution of the plurality of retrieved transactions based onpredetermined criteria.